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āļāļąāļāļĐāļ°:
Statistics, Data Analysis, SAS, English
āļāļĢāļ°āđāļ āļāļāļēāļ:
āļāļēāļāļāļĢāļ°āļāļģ
āđāļāļīāļāđāļāļ·āļāļ:
āļŠāļēāļĄāļēāļĢāļāļāđāļāļĢāļāļāđāļāđ
- Search: Experiment with text ads, bidding, and campaign structures on Google, Bing, Baidu, Naver, and other search engines. Adapt to new product features and roll out changes from successful tests.
- Display: Test, analyze, and optimize campaigns on Facebook, Twitter, Instagram, and others.
- Modeling: Analyze the vast amounts of data generated by experiments, develop models we can use for optimization, and build dashboards for account managers.
- Bachelor's Degree or higher from top university in a quantitative subject (computer science, mathematics, engineering, statistics or science).
- Ability to communicate fluently in English.
- Exposure to one or more data analysis packages or databases, e.g., SAS, R, SPSS, Python, VBA, SQL, Tableau.
- Good numerical reasoning skills.
- Proficiency in Excel.
- Intellectual curiosity and analytical skills.
- Experience in digital marketing.
- Academic research experience.
- STRA#ANLS#MRKT#3#LI-TR2.
- Experience in R studio, data modeling, hypothesis testing is a plus.
- Equal Opportunity Employer.
- At Agoda, we pride ourselves on being a company represented by people of all different backgrounds and orientations. We prioritize attracting diverse talent and cultivating an inclusive environment that encourages collaboration and innovation. Employment at Agoda is based solely on a person's merit and qualifications. We are committed to providing equal employment opportunity regardless of sex, age, race, color, national origin, religion, marital status, pregnancy, sexual orientation, gender identity, disability, citizenship, veteran or military status, and other legally protected characteristics.
- We will keep your application on file so that we can consider you for future vacancies and you can always ask to have your details removed from the file. For more details please read our privacy policy.
- To all recruitment agencies: Agoda does not accept third party resumes. Please do not send resumes to our jobs alias, Agoda employees or any other organization location. Agoda is not responsible for any fees related to unsolicited resumes.
āļāļĢāļ°āļŠāļāļāļēāļĢāļāđ:
3 āļāļĩāļāļķāđāļāđāļ
āļāļąāļāļĐāļ°:
Full Stack, Javascript, Sass
āļāļĢāļ°āđāļ āļāļāļēāļ:
āļāļēāļāļāļĢāļ°āļāļģ
āđāļāļīāļāđāļāļ·āļāļ:
āļŠāļēāļĄāļēāļĢāļāļāđāļāļĢāļāļāđāļāđ
- Maintain ownership and responsibility of mission critical systems.
- Guide one or more Agile teams to success.
- Get involved with full stack, server, and mobile app engineering and guide server, client, and infrastructure technical staff to the best solutions.
- Directly manage between 5 and 10 technology professionals and be responsible for their performance at the company.
- At least 3 years of experience managing engineering teams of 3 people and more, 5+ years of experience in software engineering.
- Proficient with web client-side technologies (React, Redux. state management, javascript, SASS, Performance optimization).
- Proficient in one or more mobile platforms (iOS, Android, Web).
- Extremely proficient in at least one programming language (JavaScript, Java, Kotlin, Scala, C#).
- Knowledge in scale, microservices and clean architecture.
- Extremely proficient in modern mobile and server coding and design practices, e.g., SOLID principals and TDD.
- Excellent people management and communication skills.
- B.S. in Computer Science or quantitative field; M.S. preferred.
- Deep experience in multiple platforms including Web, iOS, Android and API services.
- Have managed teams and been a key player at an Internet company that is at scale with large numbers of users and transactions per second.
- Have experience managing in a data driven company with experience analyzing and working with Big Data.
- Created new teams and greenfield projects solving large system problems.
- Previously worked with VP or Senior leadership at a large company.
- Worked on global projects serving world markets with distributed data centers and localization of the front end and data.
- Equal Opportunity Employer.
- At Agoda, we pride ourselves on being a company represented by people of all different backgrounds and orientations. We prioritize attracting diverse talent and cultivating an inclusive environment that encourages collaboration and innovation. Employment at Agoda is based solely on a person's merit and qualifications. We are committed to providing equal employment opportunity regardless of sex, age, race, color, national origin, religion, marital status, pregnancy, sexual orientation, gender identity, disability, citizenship, veteran or military status, and other legally protected characteristics.
- We will keep your application on file so that we can consider you for future vacancies and you can always ask to have your details removed from the file. For more details please read our privacy policy.
- To all recruitment agencies: Agoda does not accept third party resumes. Please do not send resumes to our jobs alias, Agoda employees or any other organization location. Agoda is not responsible for any fees related to unsolicited resumes.
āļāļąāļāļĐāļ°:
Statistics, Data Analysis, SAS, English
āļāļĢāļ°āđāļ āļāļāļēāļ:
āļāļēāļāļāļĢāļ°āļāļģ
āđāļāļīāļāđāļāļ·āļāļ:
āļŠāļēāļĄāļēāļĢāļāļāđāļāļĢāļāļāđāļāđ
- Search: Experiment with text ads, bidding, and campaign structures on Google, Bing, Baidu, Naver, and other search engines. Adapt to new product features and roll out changes from successful tests.
- Display: Test, analyze, and optimize campaigns on Facebook, Twitter, Instagram, and others.
- Modeling: Analyze the vast amounts of data generated by experiments, develop models we can use for optimization, and build dashboards for account managers.
- Bachelor's Degree or higher from top university in a quantitative subject (computer science, mathematics, engineering, statistics or science).
- Ability to communicate fluently in English.
- Exposure to one or more data analysis packages or databases, e.g., SAS, R, SPSS, Python, VBA, SQL, Tableau.
- Good numerical reasoning skills.
- Proficiency in Excel.
- Intellectual curiosity and analytical skills.
- Experience in digital marketing.
- Academic research experience.
- Equal Opportunity Employer.
- At Agoda, we pride ourselves on being a company represented by people of all different backgrounds and orientations. We prioritize attracting diverse talent and cultivating an inclusive environment that encourages collaboration and innovation. Employment at Agoda is based solely on a person's merit and qualifications. We are committed to providing equal employment opportunity regardless of sex, age, race, color, national origin, religion, marital status, pregnancy, sexual orientation, gender identity, disability, citizenship, veteran or military status, and other legally protected characteristics.
- We will keep your application on file so that we can consider you for future vacancies and you can always ask to have your details removed from the file. For more details please read our privacy policy.
- To all recruitment agencies: Agoda does not accept third party resumes. Please do not send resumes to our jobs alias, Agoda employees or any other organization location. Agoda is not responsible for any fees related to unsolicited resumes.
āļāļąāļāļĐāļ°:
SQL, Python, SAS
āļāļĢāļ°āđāļ āļāļāļēāļ:
āļāļēāļāļāļĢāļ°āļāļģ
āđāļāļīāļāđāļāļ·āļāļ:
āļŠāļēāļĄāļēāļĢāļāļāđāļāļĢāļāļāđāļāđ
- Manage and plan the data direction and strategy for business need.
- Drive successful of data insight initiatives and effective collaboration with stakeholders.
- Analyze business requirements and identify business problems into an analytics question and gain a deep understanding of models and algorithms capability and limitations.
- Create reports and dashboards based on data mining, evaluation, analysis, and visualization.
- Collaborate with key stakeholders including the Executive, Business Units, Data and IT teams to identify opportunities for leveraging company data to drive business solutions.
- Coordinate with the software developers, data engineers and data scientists to oversee the delivery of analytics solutions and formulate strategy for technology adoption and impact measurement.
- 5 years of experience as a data analyst or business data analyst.
- Advanced knowledge in SQL and experience working with relational databases, query authoring (SQL) as well as working familiarity with a variety of databases.
- Experience with data studio, Big Query,model,generative AI.
- Experience with R, Python, SAS, SPSS, other analytic tools.
- Experience supporting and working with cross-functional teams in a dynamic environment.
āļāļĢāļ°āļŠāļāļāļēāļĢāļāđ:
5 āļāļĩāļāļķāđāļāđāļ
āļāļąāļāļĐāļ°:
Sales, SAS, Legal
āļāļĢāļ°āđāļ āļāļāļēāļ:
āļāļēāļāļāļĢāļ°āļāļģ
āđāļāļīāļāđāļāļ·āļāļ:
āļŠāļēāļĄāļēāļĢāļāļāđāļāļĢāļāļāđāļāđ
- Creates and executes territory and account strategies to grow new software, hosting and education revenue and exceed sales quotas.
- Sells software, solutions, hosting, consulting services, and education (and combinations thereof) to current and prospective customers (new logos). Ability to execute all aspects of sales cycle (prospecting, discovery, proposal, proof, close, cross/up sell).
- Collaboratively engages with SAS Partners to sell SAS software, solutions, hosting, ...
- Educates and enables new SAS Partners by partnering with them in pipeline development activities as well as sales cycle execution.
- Creates and executes territory-specific pipeline generation plans considering value-add of SAS Partner community to maintain pipeline necessary to achieve revenue objectives. Generates pipeline through effective lead management/conversion and proactive prospecting efforts.
- Builds and leads virtual teams for opportunities of necessary internal and external resources (i.e., Customer Advisors, Consulting, Industry/Domain Experts, Legal, Enterprise Negotiations, SAS Partners, Value Engineering).
- Leads virtual team including SAS and SAS Partner resources and prospect/customer through process of thoroughly qualifying opportunities, mapping and proposing appropriate SAS offerings to buyers needs, and efficiently executing sales cycles resulting in new contracts.
- Prepares standard and nonstandard quotations and proposals leveraging a deep understanding of pricing, licensing policies, packaging, and approvals process.
- Connects regularly with existing customers to understand usage and satisfaction and uncover additional revenue opportunities.
- Triages a wide range of requests for information from current and prospective customers by connecting them with internal resources such as Customer Success.
- Keeps abreast of industry and technology trends, terminologies, software applications, operating systems, and hardware requirements.
- Engages with senior leadership on customer escalations, large opportunity strategy, and ideation around new campaigns, offerings, etc.
- Maintains and submits accurate revenue forecasts.
- Maintains accurate and up to date account, opportunity, and other information in Orion.
- Participate in workgroups related to pipeline build and sales strategy as requested by management.
- Ability to travel to critical customer or internal meetings.
- Requirements: Generally requires 5-8 years experience in Sales, Account Management with Partner Collaboration.
- Diverse and Inclusive At SAS, it s not about fitting into our culture - it s about adding to it. We believe our people make the difference. Our diverse workforce brings together unique talents and inspires teams to create amazing software that reflects the diversity of our users and customers. Our commitment to diversity is a priority to our leadership, all the way up to the top; and it s essential to who we are. To put it plainly: you are welcome here.
- SAS only sends emails from verified sas.com email addresses and never asks for sensitive, personal information or money. If you have any doubts about the authenticity of any type of communication from, or on behalf of SAS, please contact [email protected].
āļāļąāļāļĐāļ°:
Risk Management, Accounting, SAS
āļāļĢāļ°āđāļ āļāļāļēāļ:
āļāļēāļāļāļĢāļ°āļāļģ
āđāļāļīāļāđāļāļ·āļāļ:
āļŠāļēāļĄāļēāļĢāļāļāđāļāļĢāļāļāđāļāđ
- āļāļģāļŦāļāļāļāļĢāļāļāļāđāļĒāļāļēāļĒ āđāļĨāļ°āļāļĢāļ°āļāļ§āļāļāļēāļĢāļāļĢāļīāļŦāļēāļĢāļāļ§āļēāļĄāđāļŠāļĩāđāļĒāļāļāđāļēāļāļāļĨāļēāļ āļāļ§āļēāļĄāđāļŠāļĩāđāļĒāļāļāđāļēāļāļŠāļ āļēāļāļāļĨāđāļāļ āđāļĨāļ°āļāļ§āļēāļĄāđāļŠāļĩāđāļĒāļāļāļāļāļāļđāđāļāđāļēāļāļāļāļāļāļēāļāļēāļĢ āļāļēāļĄāļŦāļĨāļąāļāđāļāļāļāđāļāļāļāļāļāļēāļāļēāļĢāđāļŦāđāļāļāļĢāļ°āđāļāļĻāđāļāļĒ āđāļĨāļ°āļŠāļāļāļāļĨāđāļāļāļāļąāļāļāļĨāļĒāļļāļāļāđāļāļāļāļāļāļēāļāļēāļĢ.
- āļāļđāđāļĨāļāļēāļĢāļāļāļīāļāļąāļāļīāļāļēāļāđāļŦāđāđāļāđāļāđāļāļāļēāļĄāļāļĢāļāļāļāđāļĒāļāļēāļĒ āđāļĨāļ°āļāļĢāļ°āļāļ§āļāļāļēāļĢāļāļĢāļīāļŦāļēāļĢāļāļ§āļēāļĄāđāļŠāļĩāđāļĒāļ (āļĢāļ°āļāļļāļāļĢāļ°āđāļĄāļīāļ āļāļ§āļāļāļļāļĄ āļāļīāļāļāļēāļĄ āđāļĨāļ°āļĢāļēāļĒāļāļēāļāļāļ§āļēāļĄāđāļŠāļĩāđāļĒāļ) āļāļĩāđāļāļģāļŦāļāļ.
- āđāļŦāđāļāļ§āļēāđāļŦāđāļāđāļāļĩāđāļĒāļ§āļāļąāļāļāļēāļĢāļāļāļāđāļāļāļāļĨāļīāļāļ āļąāļāļāđāļŦāđāļāļāļāđāļēāđāļŦāđāļŠāļāļāļāļĨāđāļāļāļāļąāļāļāđāļĒāļāļēāļĒāđāļĨāļ°āļĢāļ°āđāļāļĩāļĒāļāļāļāļāļāļāļēāļāļēāļĢ.
- āļāļđāđāļĨāļāļĢāļ°āļāļ§āļāļāļēāļĢ Risk Monitoring āļāļāļāļŦāļāđāļ§āļĒāļāļēāļ āļĢāļ§āļĄāļāļķāļāļāļēāļĢāļĢāļēāļĒāļāļēāļ āđāļĨāļ°āļāļēāļĢ Alert āļāđāļāļāļđāđāļāļĢāļīāļŦāļēāļĢāļāļĩāđāđāļāļĩāđāļĒāļ§āļāđāļāļāđāļŦāđāļĄāļĩāļāļ§āļēāļĄāđāļŦāļĄāļēāļ°āļŠāļĄ.
- āļāļēāļāļāļĢāļīāļŦāļēāļĢāļāļ§āļēāļĄāđāļŠāļĩāđāļĒāļāļāļąāļāļāļĩāđāļāļ·āđāļāļāļēāļĢāļāļāļēāļāļēāļĢāđāļĨāļ°āļāļ§āļēāļĄāđāļŠāļĩāđāļĒāļāļāđāļēāļāļŠāļ āļēāļāļāļĨāđāļāļ.
- āļ§āļīāđāļāļĢāļ°āļŦāđ āļāļĢāļ°āđāļĄāļīāļ āļāļīāļāļāļēāļĄāļāļēāļĢāļāļĢāļīāļŦāļēāļĢāļāļ§āļēāļĄāđāļŠāļĩāđāļĒāļāļāđāļēāļāļāļĨāļēāļāļāļāļāļāļąāļāļāļĩāđāļāļ·āđāļāļāļēāļĢāļāļāļēāļāļēāļĢ (Banking Book) āđāļĨāļ°āļāļ§āļēāļĄāđāļŠāļĩāđāļĒāļāļāđāļēāļāļŠāļ āļēāļāļāļĨāđāļāļ āđāļĨāļ°āļāļąāļāļāļģāļĢāļēāļĒāļāļēāļāđāļŠāļāļāļāđāļāļāļđāđāļāļĢāļīāļŦāļēāļĢāđāļĨāļ°āļāļāļ°āļāļĢāļĢāļĄāļāļēāļĢāļāļĩāđāđāļāļĩāđāļĒāļ§āļāđāļāļ.
- āļāļģāļŦāļāļāđāļāļāļēāļāļāļ§āļēāļĄāđāļŠāļĩāđāļĒāļ āđāļĨāļ°āļāļĢāļ°āļŠāļēāļāļāļēāļāļāļąāļāļŦāļāđāļ§āļĒāļāļēāļāļāļĩāđāđāļāļĩāđāļĒāļ§āļāđāļāļ āđāļāļ·āđāļāļāļģāļŦāļāļāđāļāļ§āļāļēāļāļāļąāļāļāļēāļĢ / āļĨāļāļāļ§āļēāļĄāđāļŠāļĩāđāļĒāļ.
- āļāļ§āļāļāļļāļĄ āđāļĨāļ°āļāļđāđāļĨāļāļ§āļēāļĄāđāļŠāļĩāđāļĒāļāļāđāļēāļāļāļĨāļēāļāļāļāļāļāļąāļāļāļĩāđāļāļ·āđāļāļāļēāļĢāļāļāļēāļāļēāļĢ āđāļĨāļ°āļāļ§āļēāļĄāđāļŠāļĩāđāļĒāļāļāđāļēāļāļŠāļ āļēāļāļāļĨāđāļāļāđāļŦāđāļāļĒāļđāđāļ āļēāļĒāđāļāđāđāļāļāļēāļ āļāļ§āļēāļĄāđāļŠāļĩāđāļĒāļāļāļĩāđāļāļāļēāļāļēāļĢāļĒāļāļĄāļĢāļąāļāđāļāđ.
- āļāļąāļāļāļģāļāļąāļāļāļĩāļāđāļāļāļāļąāļāļāļ§āļēāļĄāđāļŠāļĩāđāļĒāļ (Hedge Accounting) āļāļĩāđāđāļāļĩāđāļĒāļ§āļāđāļāļāļāļąāļāļāļēāļĢāļāļĢāļīāļŦāļēāļĢāļāļ§āļēāļĄāđāļŠāļĩāđāļĒāļ.
- āļāļēāļāļāļĢāļīāļŦāļēāļĢāļāļ§āļēāļĄāđāļŠāļĩāđāļĒāļāļāļąāļāļāļĩāđāļāļ·āđāļāļāļēāļĢāļāđāļē.
- āļ§āļīāđāļāļĢāļēāļ°āļŦāđ āļāļĢāļ°āđāļĄāļīāļ āļāļīāļāļāļēāļĄāļāļ§āļēāļĄāđāļŠāļĩāđāļĒāļāļāđāļēāļāļāļĨāļēāļāļāļāļāļāļąāļāļāļĩāđāļāļ·āđāļāļāļēāļĢāļāđāļē (Trading Book) āđāļĨāļ°āļāļąāļāļāļģāļĢāļēāļĒāļāļēāļāđāļŠāļāļāļāđāļ āļāļđāđāļāļĢāļīāļŦāļēāļĢāđāļĨāļ°āļāļāļ°āļāļĢāļĢāļĄāļāļēāļĢāļāļĩāđāđāļāļĩāđāļĒāļ§āļāđāļāļ.
- āļāļģāļŦāļāļāđāļāļāļēāļāļāļ§āļēāļĄāđāļŠāļĩāđāļĒāļāļāđāļēāļāļāļĨāļēāļāļāļāļāļāļąāļāļāļĩāđāļāļ·āđāļāļāļēāļĢāļāđāļē āđāļĨāļ°āļāļĢāļ°āļŠāļēāļāļāļēāļāļāļąāļāļŦāļāđāļ§āļĒāļāļēāļāļāļĩāđāđāļāļĩāđāļĒāļ§āļāđāļāļ āđāļāļ·āđāļāļāļģāļŦāļāļ āđāļāļ§āļāļēāļāļāļąāļāļāļēāļĢ / āļĨāļāļāļ§āļēāļĄāđāļŠāļĩāđāļĒāļ.
- āļāļ§āļāļāļļāļĄ āđāļĨāļ°āļāļđāđāļĨāļāļ§āļēāļĄāđāļŠāļĩāđāļĒāļāļāđāļēāļāļāļĨāļēāļāļāļāļāļāļąāļāļāļĩāđāļāļ·āđāļāļāļēāļĢāļāđāļēāđāļŦāđāļāļĒāļđāđāļ āļēāļĒāđāļāđāđāļāļāļēāļāļāļ§āļēāļĄāđāļŠāļĩāđāļĒāļāļāļĩāđāļāļāļēāļāļēāļĢāļĒāļāļĄāļĢāļąāļāđāļāđ.
- āđāļŦāđāļāļģāđāļāļ°āļāļģ āđāļĨāļ°āļāļģāļāļĢāļķāļāļĐāļēāđāļāļĩāđāļĒāļ§āļāļąāļāļāļ§āļēāļĄāđāļŠāļĩāđāļĒāļāļāđāļēāļāļāļĨāļēāļāļŠāļģāļŦāļĢāļąāļāļāļĨāļīāļāļ āļąāļāļāđāļāļēāļāļāļēāļĢāđāļāļīāļāļāļĩāđāļāļāļāđāļŦāļĄāđ āļāļēāļĢāļāļąāļāļāļēāļĢāļđāļāđāļāļ āļāļĨāļīāļāļ āļąāļāļāđ āđāļĨāļ°āļāļĢāļ°āļāļ§āļāļāļēāļĢāļāļĢāļīāļŦāļēāļĢāļāļ§āļēāļĄāđāļŠāļĩāđāļĒāļ.
- āļāļĢāļ°āđāļĄāļīāļāđāļāļīāļāļāļāļāļāļļāļāđāļāļ·āđāļāļĢāļāļāļĢāļąāļāļāļ§āļēāļĄāđāļŠāļĩāđāļĒāļāļāđāļēāļāļāļĨāļēāļāđāļŦāđāđāļāđāļāđāļāļāļēāļĄāļāļĩāđāļāļēāļāļāļēāļĢāļāļģāļŦāļāļ.
- āļāļēāļāļāļĢāļīāļŦāļēāļĢāļāļ§āļēāļĄāđāļŠāļĩāđāļĒāļāļāļāļāļāļđāđāļāđāļē.
- āļ§āļīāđāļāļĢāļēāļ°āļŦāđ āļāļĢāļ°āđāļĄāļīāļ āđāļĨāļ°āļāļīāļāļāļēāļĄāļāļēāļĢāļāļĢāļīāļŦāļēāļĢāļāļ§āļēāļĄāđāļŠāļĩāđāļĒāļāļāļāļāļāļđāđāļāđāļē āđāļĨāļ°āļāļąāļāļāļģāļĢāļēāļĒāļāļēāļāđāļŠāļāļāļāđāļāļāļđāđāļāļĢāļīāļŦāļēāļĢāđāļĨāļ°āļāļāļ°āļāļĢāļĢāļĄāļāļēāļĢāļāļĩāđāđāļāļĩāđāļĒāļ§āļāđāļāļ.
- āļāļđāđāļĨāļĢāļ°āļāļāļāļēāļāļāļāļāļŦāđāļāļāļāđāļēāđāļāļŠāđāļ§āļāļāļĩāđāđāļāļĩāđāļĒāļ§āļāđāļāļāļāļąāļāļāļēāļāļāļĢāļīāļŦāļēāļĢāļāļ§āļēāļĄāđāļŠāļĩāđāļĒāļāļāļāļāļāļđāđāļāđāļē.
- āđāļŦāđāļāļģāđāļāļ°āļāļģ āđāļĨāļ°āļāļģāļāļĢāļķāļāļĐāļēāđāļāļĩāđāļĒāļ§āļāļąāļāļāļļāļĢāļāļĢāļĢāļĄāļāļāļāļŦāđāļāļāļāđāļēāļāļĩāđāļĄāļĩāļāļ§āļēāļĄāđāļŠāļĩāđāļĒāļāļāļāļāļāļđāđāļāđāļē āļŠāļģāļŦāļĢāļąāļāļāļĨāļīāļāļ āļąāļāļāđāļāļēāļāļāļēāļĢāđāļāļīāļāļāļĩāđāļāļāļāđāļŦāļĄāđ āļāļēāļĢāļāļąāļāļāļēāļĢāļđāļāđāļāļāļāļĨāļīāļāļ āļąāļāļāđ āđāļĨāļ°āļāļĢāļ°āļāļ§āļāļāļēāļĢāļāļĢāļīāļŦāļēāļĢāļāļ§āļēāļĄāđāļŠāļĩāđāļĒāļāļāļāļāļāļđāđāļāđāļē.
- āļāļĢāļ°āđāļĄāļīāļāļāđāļēāļāļ§āļēāļĄāđāļŠāļĩāđāļĒāļāļāļēāļāļāļēāļĢāļāļĢāļąāļāļāļļāļāļ āļēāļāđāļāļĢāļāļīāļāļāļāļāļāļđāđāļŠāļąāļāļāļē (Credit Valuation Adjustment: CVA) āļāļąāđāļāđāļāļŠāđāļ§āļāļāļāļ Pre-deal CVA āļŠāļģāļŦāļĢāļąāļāļāļĢāļ°āļāļāļāļāļēāļĢ Pricing āļāļļāļĢāļāļĢāļĢāļĄāļāļāļļāļāļąāļāļāđ āđāļĨāļ° Accounting CVA āļāļēāļĄāļĄāļēāļāļĢāļāļēāļāļĢāļēāļĒāļāļēāļāļāļēāļāļāļēāļĢāđāļāļīāļāļŠāļģāļŦāļĢāļąāļāļāļģāļŠāđāļāļāđāļēāļĒāļāļēāļĢāļāļąāļāļāļĩ.
- āļāļģāļāļ§āļāļĄāļđāļĨāļāđāļēāđāļāļĩāļĒāļāđāļāđāļēāļŠāļīāļāļāļĢāļąāļāļĒāđāđāļāļāļāļāļļāļĨ (Credit Equivalent Amount-CEA) āļŠāļģāļŦāļĢāļąāļāļāļļāļĢāļāļĢāļĢāļĄ Derivatives āđāļĨāļ°āļāļļāļĢāļāļĢāļĢāļĄ Repurchase Agreement āđāļāļ·āđāļāļāļĢāļ°āļāļāļāļāļēāļĢāļāļģāļāļ§āļāļŠāļīāļāļāļĢāļąāļāļĒāđāđāļŠāļĩāđāļĒāļāļāđāļēāļāđāļāļĢāļāļīāļāļāļāļāļāļđāđāļŠāļąāļāļāļēāđāļĨāļ°āļŦāļĨāļąāļāđāļāļāļāđāļāļēāļĢāļāļģāļāļąāļāļĨāļđāļāļŦāļāļĩāđāļĢāļēāļĒāđāļŦāļāđ (Single Lending Limit).
- āļāļēāļāļŠāļāļąāļāļŠāļāļļāļāļĢāļ°āļāļāļāļēāļ.
- āļāļģāļŦāļāđāļēāļāļĩāđ Administrator āđāļĨāļ° Setup Parameter āļĢāļ°āļāļāļāļēāļāļāļĩāđāđāļāļĩāđāļĒāļ§āļāđāļāļāļāļąāļāļāļēāļĢāļāļĢāļīāļŦāļēāļĢāļāļ§āļēāļĄāđāļŠāļĩāđāļĒāļāļāļĩāđāļĢāļąāļāļāļīāļāļāļāļāđāļāļĒ āļāđāļēāļĒāļāļĢāļīāļŦāļēāļĢāļāļ§āļēāļĄāđāļŠāļĩāđāļĒāļāļāđāļēāļāļāļĨāļēāļ āđāļāđāļ KRM, āļĢāļ°āļāļāļŦāđāļāļāļāđāļēāđāļāļīāļ, Reuters āđāļĨāļ° Bloomberg āđāļāđāļāļāđāļ.
- āļāļĢāļ°āļŠāļēāļāļāļēāļāļĢāļ°āļŦāļ§āđāļēāļāļāļđāđāđāļāđāđāļĨāļ°āļŦāļāđāļ§āļĒāļāļēāļ IT āđāļāļ·āđāļāđāļŦāđāļāļēāļĢāļŠāļāļąāļāļŠāļāļļāļāļāļļāļāļāđāļēāļāđāļāļĩāđāļĒāļ§āļāļąāļāļāļēāļĢāđāļāđāļāļēāļāļĢāļ°āļāļāļāļēāļāļāļĨāļāļāļāļāļāļēāļĢ āļāļĢāļąāļāļāļĢāļļāļ āđāļāđāđāļāļĢāļ°āļāļāļāļēāļ.
- āļāļĢāļ§āļāļŠāļāļāļāđāļāļĄāļđāļĨāļāļąāļāļĢāļ°āļāļāļāļēāļāļāļĩāđāđāļāļĩāđāļĒāļ§āļāđāļāļ āļāļĢāļ°āļāļāļĒāļāļ āđāļĨāļ°āđāļāđāđāļ/āļāļĢāļąāļāļāļĢāļļāļ Market Data āđāļŦāđāļāļđāļāļāđāļāļāđāļŦāļĄāļēāļ°āļŠāļĄ.
- āļāļēāļāļāļĢāļ°āđāļĄāļīāļāļĄāļđāļĨāļāđāļēāļāļļāļĢāļāļĢāļĢāļĄāđāļĨāļ°āļāđāļāļĄāļđāļĨ.
- āļĻāļķāļāļĐāļēāļ§āļīāđāļāļĢāļēāļ°āļŦāđāļāļ§āļēāļĄāļāđāļāļāļāļēāļĢ Data āđāļāļ·āđāļāđāļāđāļāļĢāļīāļŦāļēāļĢāļāļ§āļēāļĄāđāļŠāļĩāđāļĒāļ āļĢāļ§āļĄāļāļķāļāļāļēāļĢāļāļąāļāļāļģ Data Specification āđāļāđāļ Position Data āđāļĨāļ° Market Data āđāļāđāļāļāđāļ āđāļāļ·āđāļāļĢāļāļāļĢāļąāļāļāļēāļĢāļāļĢāļīāļŦāļēāļĢāļāļ§āļēāļĄāđāļŠāļĩāđāļĒāļāļāđāļēāļāļāļĨāļēāļ āļāļ§āļģāļĄāđāļŠāļĩāđāļĒāļāļāđāļēāļāļŠāļ āļēāļ āļāļĨāđāļāļ āđāļĨāļ°āļāļ§āļēāļĄāđāļŠāļĩāđāļĒāļāļāļāļāļāļđāđāļāđāļē āđāļĨāļ°āļāļēāļĢāļāļģāļāļ§āļāđāļāļīāļāļāļāļāļāļļāļāđāļāļ·āđāļāļĢāļāļāļĢāļąāļāļāļ§āļēāļĄāđāļŠāļĩāđāļĒāļāļāđāļēāļāļāļĨāļēāļ.
- āļāļĢāļ§āļāļŠāļāļāļāđāļāļĄāļđāļĨāļāļąāļāļĢāļ°āļāļāļāļēāļ/āļĢāļēāļĒāļāļēāļāļāļĩāđāđāļāļĩāđāļĒāļ§āļāđāļāļ āļĢāļ§āļĄāļāļķāļāļāļĢāļ°āļāļāļĒāļāļ āđāļĨāļ°āđāļāđāđāļ/āļāļĢāļąāļāļāļĢāļļāļ Position Data āđāļŦāđāļāļđāļāļāđāļāļāđāļŦāļĄāļēāļ°āļŠāļĄ.
- āļāļĢāļ°āđāļĄāļīāļ āđāļĨāļ°āļāļĢāļ§āļāļŠāļāļāļ§āļīāļāļĩāļāļēāļĢāļāļĢāļ°āđāļĄāļīāļāļĄāļđāļĨāļāđāļē (Pricing) āļāļāļāļĢāļ°āļāļāļāļēāļāļāļāļāļŦāđāļāļāļāđāļē āļāļĢāđāļāļĄāļāļąāđāļāļāļĢāļ°āđāļĄāļīāļāļĄāļđāļĨāļāđāļēāļāļēāļĄāļĢāļēāļāļēāļāļĨāļēāļ (Mark to Market) āđāļĨāļ°āļāļēāļĄāđāļāļāļāļģāļĨāļāļ (Mark to Model) āļāļāļāļāļļāļĢāļāļĢāļĢāļĄāļāļĩāđāđāļāļĩāđāļĒāļ§āļāđāļāļ.
- āļāļāļīāļāļąāļāļīāļāļēāļāļāļĩāđāđāļāļĩāđāļĒāļ§āļāđāļāļāļ āļēāļĒāđāļāđāļŠāļąāļāļāļē Credit Support Annex (CSA) āđāļāđāļ āļāļāļāļ§āļāļāļ§āļēāļĄāđāļŦāļĄāļēāļ°āļŠāļĄāļāļāļāļāļĨ Mark to Market āļāļāļāļāļđāđāļŠāļąāļāļāļē āđāļāđāļāļāđāļ.
- āļ§āļļāļāļīāļāļēāļĢāļĻāļķāļāļĐāļēāļĢāļ°āļāļąāļāļāļĢāļīāļāļāļēāļāļĢāļĩāļāļķāđāļāđāļ āļāđāļēāļāļāļĢāļīāļŦāļēāļĢāļāļļāļĢāļāļīāļ āđāļĻāļĢāļĐāļāļĻāļēāļŠāļāļĢāđ āļŠāļēāļāļēāļāļēāļĢāļāļąāļāļāļĩ āļŠāļēāļāļēāļāļēāļĢāđāļāļīāļ āļŠāļēāļāļēāļŠāļāļīāļāļī āļŠāļēāļāļēāļāļāļĄāļāļīāļ§āđāļāļāļĢāđ āļŠāļēāļāļēāļāļēāļĢāļāļĨāļēāļ āļŦāļĢāļ·āļāļŠāļēāļāļēāļāļĩāđāđāļāļĩāđāļĒāļ§āļāđāļāļ.
- āļĄāļĩāļāļ§āļēāļĄāļĢāļāļāļĢāļđāđāđāļāļāļļāļĢāļāļīāļāļāļāļēāļāļēāļĢ āļāļĨāļīāļāļ āļąāļāļāđāđāļĨāļ°āļāļĢāļīāļāļēāļĢ āļĢāļ§āļĄāļāļķāļāļĄāļĩāļāļ§āļēāļĄāļĢāļđāđāļāđāļēāļāļŠāļīāļāđāļāļ·āđāļāļāļēāļĢāļāļĨāļēāļ āđāļĨāļ°āļāļēāļĢāļāļĢāļīāļŦāļēāļĢāļāļąāļāļāļēāļĢāļāļ§āļēāļĄāđāļŠāļĩāđāļĒāļāļāļĩāđāļāļĒāļđāđāđāļāļāļ§āļēāļĄāļĢāļąāļāļāļīāļāļāļāļ.
- āļĄāļĩāļāļ§āļēāļĄāļĢāļđāđāļāļ§āļēāļĄāđāļāđāļēāđāļāđāļāļāļ āļĢāļ°āđāļāļĩāļĒāļ āļāđāļĒāļāļēāļĒ āļŦāļĨāļąāļāđāļāļāļāđāļĄāļēāļāļĢāļāļēāļāļŠāļēāļāļĨ āļāđāļēāļāļāļēāļĢāļāļĢāļīāļŦāļēāļĢāļāļ§āļēāļĄāđāļŠāļĩāđāļĒāļ.
- āļĄāļĩāļāļ§āļēāļĄāļĢāļđāđāļāđāļēāļāļāļĨāļēāļāđāļāļīāļāđāļĨāļ°āļāļĨāļēāļāļāļļāļ.
- āļŠāļēāļĄāļēāļĢāļāđāļāđāļ āļēāļĐāļēāļāļąāļāļāļĪāļĐāđāļāđāđāļāļĢāļ°āļāļąāļāļāļĩāļŠāļēāļĄāļēāļĢāļāđāļāđāļāļāļĄāļāļīāļ§āđāļāļāļĢāđāđāļāđāđāļāđāļāļāļĒāđāļēāļāļāļĩ.
- āļŠāļēāļĄāļēāļĢāļāđāļāđāđāļāļĢāđāļāļĢāļĄ R, SAS, SQL, MS āđāļāļīāļāļĨāļķāļ āđāļĨāļ° Python āđāļāđāļāļāđāļ..
- āļāđāļēāļāļŠāļēāļĄāļēāļĢāļāļāđāļēāļāđāļĨāļ°āļĻāļķāļāļĐāļēāļāđāļĒāļāļēāļĒāļāļ§āļēāļĄāđāļāđāļāļŠāđāļ§āļāļāļąāļ§āļāļāļāļāļāļēāļāļēāļĢāļāļĢāļļāļāđāļāļĒ āļāļģāļāļąāļ (āļĄāļŦāļēāļāļ) āļāļĩāđ https://krungthai.com/th/content/privacy-policy āļāļąāđāļāļāļĩāđ āļāļāļēāļāļēāļĢāđāļĄāđāļĄāļĩāđāļāļāļāļēāļŦāļĢāļ·āļāļāļ§āļēāļĄāļāļģāđāļāđāļāđāļāđ āļāļĩāđāļāļ°āļāļĢāļ°āļĄāļ§āļĨāļāļĨāļāđāļāļĄāļđāļĨāļŠāđāļ§āļāļāļļāļāļāļĨāļāļĩāđāļĄāļĩāļāļ§āļēāļĄāļāđāļāļāđāļŦāļ§ āļĢāļ§āļĄāļāļķāļāļāđāļāļĄāļđāļĨāļāļĩāđāđāļāļĩāđāļĒāļ§āļāđāļāļāļĻāļēāļŠāļāļēāđāļĨāļ°/āļŦāļĢāļ·āļāļŦāļĄāļđāđāđāļĨāļŦāļīāļ āļāļķāđāļāļāļēāļāļāļĢāļēāļāļāļāļĒāļđāđāđāļāļŠāļģāđāļāļēāļāļąāļāļĢāļāļĢāļ°āļāļģāļāļąāļ§āļāļĢāļ°āļāļēāļāļāļāļāļāļāđāļēāļāđāļāđāļāļĒāđāļēāļāđāļ āļāļąāļāļāļąāđāļ āļāļĢāļļāļāļēāļāļĒāđāļēāļāļąāļāđāļŦāļĨāļāđāļāļāļŠāļēāļĢāđāļāđ āļĢāļ§āļĄāļāļķāļāļŠāļģāđāļāļēāļāļąāļāļĢāļāļĢāļ°āļāļģāļāļąāļ§āļāļĢāļ°āļāļēāļāļ āļŦāļĢāļ·āļāļāļĢāļāļāļāđāļāļĄāļđāļĨāļŠāđāļ§āļāļāļļāļāļāļĨāļāļĩāđāļĄāļĩāļāļ§āļēāļĄāļāđāļāļāđāļŦāļ§āļŦāļĢāļ·āļāļāđāļāļĄāļđāļĨāļāļ·āđāļāđāļ āļāļķāđāļāđāļĄāđāđāļāļĩāđāļĒāļ§āļāđāļāļāļŦāļĢāļ·āļāđāļĄāđāļāļģāđāļāđāļāļŠāļģāļŦāļĢāļąāļāļ§āļąāļāļāļļāļāļĢāļ°āļŠāļāļāđāđāļāļāļēāļĢāļŠāļĄāļąāļāļĢāļāļēāļāđāļ§āđāļāļāđāļ§āđāļāđāļāļāđ āļāļāļāļāļēāļāļāļĩāđ āļāļĢāļļāļāļēāļāļģāđāļāļīāļāļāļēāļĢāđāļŦāđāđāļāđāđāļāļ§āđāļēāđāļāđāļāļģāđāļāļīāļāļāļēāļĢāļĨāļāļāđāļāļĄāļđāļĨāļŠāđāļ§āļāļāļļāļāļāļĨāļāļĩāđāļĄāļĩāļāļ§āļēāļĄāļāđāļāļāđāļŦāļ§ (āļāđāļēāļĄāļĩ) āļāļāļāļāļēāļāđāļĢāļāļđāđāļĄāđāđāļĨāļ°āđāļāļāļŠāļēāļĢāļāļ·āđāļāđāļāļāđāļāļāļāļĩāđāļāļ°āļāļąāļāđāļŦāļĨāļāđāļāļāļŠāļēāļĢāļāļąāļāļāļĨāđāļēāļ§āđāļ§āđāļāļāđāļ§āđāļāđāļāļāđāđāļĨāđāļ§āļāđāļ§āļĒ āļāļąāđāļāļāļĩāđ āļāļāļēāļāļēāļĢāļĄāļĩāļāļ§āļēāļĄāļāļģāđāļāđāļāļāđāļāļāđāļāđāļāļĢāļ§āļāļĢāļ§āļĄāļāđāļāļĄāļđāļĨāļŠāđāļ§āļāļāļļāļāļāļĨāđāļāļĩāđāļĒāļ§āļāļąāļāļāļĢāļ°āļ§āļąāļāļīāļāļēāļāļāļēāļāļĢāļĢāļĄāļāļāļāļāđāļēāļāđāļāļ·āđāļāļāļĢāļĢāļĨāļļāļ§āļąāļāļāļļāļāļĢāļ°āļŠāļāļāđāđāļāļāļēāļĢāļāļīāļāļēāļĢāļāļēāļĢāļąāļāļāļļāļāļāļĨāđāļāđāļēāļāļģāļāļēāļ āļŦāļĢāļ·āļāļāļēāļĢāļāļĢāļ§āļāļŠāļāļāļāļļāļāļŠāļĄāļāļąāļāļī āļĨāļąāļāļĐāļāļ°āļāđāļāļāļŦāđāļēāļĄ āļŦāļĢāļ·āļāļāļīāļāļēāļĢāļāļēāļāļ§āļēāļĄāđāļŦāļĄāļēāļ°āļŠāļĄāļāļāļāļāļļāļāļāļĨāļāļĩāđāļāļ°āđāļŦāđāļāļģāļĢāļāļāļģāđāļŦāļāđāļ āļāļķāđāļāļāļēāļĢāđāļŦāđāļāļ§āļēāļĄāļĒāļīāļāļĒāļāļĄāđāļāļ·āđāļāđāļāđāļāļĢāļ§āļāļĢāļ§āļĄ āđāļāđ āļŦāļĢāļ·āļāđāļāļīāļāđāļāļĒāļāđāļāļĄāļđāļĨāļŠāđāļ§āļāļāļļāļāļāļĨāđāļāļĩāđāļĒāļ§āļāļąāļāļāļĢāļ°āļ§āļąāļāļīāļāļēāļāļāļēāļāļĢāļĢāļĄāļāļāļāļāđāļēāļāļĄāļĩāļāļ§āļēāļĄāļāļģāđāļāđāļāļŠāļģāļŦāļĢāļąāļāļāļēāļĢāđāļāđāļēāļāļģāļŠāļąāļāļāļēāđāļĨāļ°āļāļēāļĢāđāļāđāļĢāļąāļāļāļēāļĢāļāļīāļāļēāļĢāļāļēāļāļēāļĄāļ§āļąāļāļāļļāļāļĢāļ°āļŠāļāļāđāļāļąāļāļāļĨāđāļēāļ§āļāđāļēāļāļāđāļ āđāļāļāļĢāļāļĩāļāļĩāđāļāđāļēāļāđāļĄāđāđāļŦāđāļāļ§āļēāļĄāļĒāļīāļāļĒāļāļĄāđāļāļāļēāļĢāđāļāđāļāļĢāļ§āļāļĢāļ§āļĄ āđāļāđ āļŦāļĢāļ·āļāđāļāļīāļāđāļāļĒāļāđāļāļĄāļđāļĨāļŠāđāļ§āļāļāļļāļāļāļĨāđāļāļĩāđāļĒāļ§āļāļąāļāļāļĢāļ°āļ§āļąāļāļīāļāļēāļāļāļēāļāļĢāļĢāļĄ āļŦāļĢāļ·āļāļĄāļĩāļāļēāļĢāļāļāļāļāļ§āļēāļĄāļĒāļīāļāļĒāļāļĄāđāļāļ āļēāļĒāļŦāļĨāļąāļ āļāļāļēāļāļēāļĢāļāļēāļāđāļĄāđāļŠāļēāļĄāļēāļĢāļāļāļģāđāļāļīāļāļāļēāļĢāđāļāļ·āđāļāļāļĢāļĢāļĨāļļāļ§āļąāļāļāļļāļāļĢāļ°āļŠāļāļāđāļāļąāļāļāļĨāđāļēāļ§āļāđāļēāļāļāđāļāđāļāđ āđāļĨāļ°āļāļēāļ āļāļģāđāļŦāđāļāđāļēāļāļŠāļđāļāđāļŠāļĩāļĒāđāļāļāļēāļŠāđāļāļāļēāļĢāđāļāđāļĢāļąāļāļāļēāļĢāļāļīāļāļēāļĢāļāļēāļĢāļąāļāđāļāđāļēāļāļģāļāļēāļāļāļąāļāļāļāļēāļāļēāļĢ ".
āļāļĢāļ°āļŠāļāļāļēāļĢāļāđ:
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āļāļĢāļ°āđāļ āļāļāļēāļ:
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āđāļāļīāļāđāļāļ·āļāļ:
āļŠāļēāļĄāļēāļĢāļāļāđāļāļĢāļāļāđāļāđ
- āļāļģāļŦāļāļāļāļĢāļāļāđāļāļ§āļāļēāļāļāļēāļĢāļāļĢāļąāļāļāļĢāļļāļāđāļāļĢāļāļŠāļĢāđāļēāļāļŦāļāļĩāđāđāļĨāļ°āļāļģāđāļāļīāļāļāļāļĩāļāļēāļĄāļāđāļĒāļāļēāļĒāļāļāļāļāļāļēāļāļēāļĢāđāļŦāđāļāļāļĢāļ°āđāļāļĻāđāļāļĒ āđāļĨāļ° āļŦāļāđāļ§āļĒāļāļēāļāļāļĩāđāļāļģāļāļąāļāļāļđāđāļĨāđāļāļ·āđāļāđāļŠāļāļāļāļāļāļāļļāļĄāļąāļāļīāđāļāļĨāļĩāđāļĒāļāđāļāļĨāļāļāđāļāļāļāļ°āļāļĢāļĢāļĄāļāļēāļĢāļāļĩāđāđāļāļĩāđāļĒāļ§āļāđāļāļ āļĢāļ§āļĄāļāļķāļāļāļ§āļāļāļļāļĄāļāļĢāļ°āļāļ§āļāļāļēāļĢāļāļēāļĢāļāļāļīāļāļąāļāļīāļāļēāļāđāļŦāđāļĄāļĩāļāļ§āļēāļĄāļŠāļāļāļāļĨāđāļāļāļāļąāļ.
- āļāļąāļāļāļģāļāđāļāļĄāļđāļĨāđāļāļāļēāļĢāļāļ§āļāļāļļāļĄāđāļĨāļ°āļāļĢāļ§āļāļŠāļāļāļāļ§āļēāļĄāļāļđāļāļāđāļāļāđāļāđāļāļ·āđāļāļāļāđāļāļāļāļāļāļēāļĢāļ§āļīāđāļāļĢāļēāļ°āļŦāđāđāļĨāļ°āļāļāļļāļĄāļąāļāļīāļŠāļīāļāđāļāļ·āđāļ āđāļāļ·āđāļāļĨāļāļāļ§āļēāļĄāđāļŠāļĩāđāļĒāļāđāļĨāļ°āļāļąāļāļĢāļēāļāļēāļĢāđāļāļīāļ NPLs.
- āļ§āļīāđāļāļĢāļēāļ°āļŦāđāđāļĨāļ°āļāļĢāļ°āđāļĄāļīāļāļāļĨāļāļēāļĢāļāļģāđāļāļīāļāļāļēāļ āļāļēāļĢāļāļĢāļīāļŦāļēāļĢāļāļ§āļēāļĄāđāļŠāļĩāđāļĒāļāļāđāļēāļāļŠāļīāļāđāļāļ·āđāļ(Credit Risk)āđāļāļĄāļīāļāļīāļāđāļēāļāđ āđāļāļ·āđāļāđāļāđāļāļĢāļ°āļāļāļāļāļēāļĢāļāļĢāļ°āđāļĄāļīāļāļāļ§āļēāļĄāđāļŠāļĩāđāļĒāļāđāļāļāļēāļĢāļāļāļāļāļĨāļīāļāļ āļąāļāļāđāļŠāļīāļāđāļāļ·āđāļ āđāļĨāļ°āļāļĨāļīāļāļ āļąāļāļāđāļāļ·āđāļāđ ...
- āļāļēāļāļāļģāļāļēāļāļāļāļļāļĄāļąāļāļī (Delegation Authority).
- āļāļģāļŦāļāļāļāļģāļāļēāļāļāļāļļāļĄāļąāļāļīāļŠāļīāļāđāļāļ·āđāļāđāļĨāļ°āļāļĢāļąāļāļāļĢāļļāļāđāļāļĢāļāļŠāļĢāđāļēāļāļŦāļāļĩāđ āđāļĨāļ°āļāļāļāļ§āļāļāļģāļāļēāļāđāļāļāļēāļĢāļāļģāļāļļāļĢāļāļĢāļĢāļĄāļāļĩāđāđāļāļĩāđāļĒāļ§āļāđāļāļ āđāļāļ·āđāļāđāļŦāđ āļŠāļāļāļāļĨāđāļāļāļāļąāļāļāļēāļĢāļāļĢāļīāļŦāļēāļĢāļāļ§āļēāļĄāđāļŠāļĩāđāļĒāļāđāļĨāļ°āļāļ§āļēāļĄāļāļĨāđāļāļāļāļąāļ§āđāļāļāļēāļĢāļāļāļīāļāļąāļāļīāļāļēāļ.
- āļāļđāđāļĨāđāļĨāļ°āļāļ§āļāļāļļāļĄāļāļēāļĢāļāļģāđāļāļīāļāļāļēāļāļāļēāļĄāļāļĢāļāļāļāļģāļāļēāļāļāļāļļāļĄāļąāļāļīāļŠāļīāļāđāļāļ·āđāļāđāļĨāļ°āļāļĢāļąāļāļāļĢāļļāļāđāļāļĢāļāļŠāļĢāđāļēāļāļŦāļāļĩāđ āđāļĨāļ°āļāļļāļĢāļāļĢāļĢāļĄāļŠāļīāļāđāļāļ·āđāļ āđāļāļ·āđāļāđāļŦāđ āļĢāļ°āļāļāļāļēāļāļāļĩāđāđāļāļĩāđāļĒāļ§āļāđāļāļāļāļģāļŦāļāļāđāļāđāļāļĒāđāļēāļāļāļđāļāļāđāļāļ.
- āļāļāļāļāđāļāļŦāļēāļĢāļ·āļāļāļąāļāļŦāļāđāļ§āļĒāļāļēāļāļ āļēāļĒāđāļāđāļĨāļ°āļŦāļāđāļ§āļĒāļāļēāļāļ āļēāļĒāļāļāļāļāļāļēāļāļēāļĢ āđāļāļ·āđāļāđāļŦāđāļŦāļāđāļ§āļĒāļāļēāļāļāļĩāđāđāļāļĩāđāļĒāļ§āļāđāļāļāļāļāļīāļāļąāļāļīāļāļēāļāđāļāđāļāļĒāđāļēāļāļāļđāļāļāđāļāļ.
- āļāļēāļ Retail Credit Risk Portfolio Management.
- āļāļģāļŦāļāļāļāļĨāļĒāļļāļāļāđāđāļāļāļēāļĢāļāļđāđāļĨāđāļĨāļ°āļāļąāļāļāļģāļĢāļēāļĒāļāļēāļ Retail Credit Risk Portfolio Management āļāļĢāļāļāļāļĨāļļāļĄāļāļąāđāļāđāļāđāļāļēāļĢāļāļģāļŦāļāļāļāļąāļ§āļāļĩāđāļ§āļąāļāļāļ§āļēāļĄāđāļŠāļĩāđāļĒāļāļāđāļēāļāđ āļāļāļāđāļāđāļĨāļ°āļāļĨāļīāļāļ āļąāļāļāđ āđāļāļ·āđāļāļāļģāđāļŠāļāļāļāđāļāļāļāļ°āļāļĢāļĢāļĄāļāļēāļĢāļāļĩāđāđāļāļĩāđāļĒāļ§āļāđāļāļ.
- āļāļģāļŦāļāļāļāļĨāļĒāļļāļāļāđāđāļĨāļ°āļāļąāļāļāļģ Strategic Planning āđāļāļ·āđāļāļāļąāļāļāļēāļĢāļāļđāđāļĨāļāļĨāļāļēāļĢāļāļģāđāļāļīāļāļāļēāļāđāļāđāļĢāļ·āđāļāļ Credit Loss and Profitability āđāļŦāđāđāļāđāļāđāļāļāļēāļĄāđāļāļāļāļĩāđāļāļģāļŦāļāļ.
- āļāļĢāļ°āđāļĄāļīāļ Risk & Return āđāļāļĄāļīāļāļīāļāđāļēāļāđ āđāļāļ·āđāļāđāļāđāļāļĢāļ°āļāļāļāļāļēāļĢāđāļŦāđāļāļ§āļēāļĄāđāļŦāđāļāđāļāļāļēāļĢāļāļāļāļāļĨāļīāļāļ āļąāļāļāđāļŠāļīāļāđāļāļ·āđāļ.
- āļāļąāļāļāļģāļāđāļāļĄāļđāļĨāļāļēāļĄāļāļĩāđāļāļģāļŦāļāļāļāļēāļāļāļāļēāļāļēāļĢāđāļŦāđāļāļāļĢāļ°āđāļāļĻāđāļāļĒ āļŦāļāđāļ§āļĒāļāļēāļāļ āļēāļĒāļāļāļāđāļĨāļ°āļŦāļāđāļ§āļĒāļāļēāļāļ āļēāļĒāđāļāļāļāļēāļāļēāļĢ āđāļĨāļ° āļ§āļīāđāļāļĢāļēāļ°āļŦāđāļāđāļāļĄāļđāļĨāđāļāļ·āđāļāļŠāļāļąāļāļŠāļāļļāļāļāđāļāļĄāļđāļĨāļ āļēāļĒāđāļāļŠāļēāļĒāļāļēāļāđāļāļĩāļĒāļ§āļāļąāļ.
- āļ§āļļāļāļīāļāļēāļĢāļĻāļķāļāļĐāļēāļĢāļ°āļāļąāļāļāļĢāļīāļāļāļēāļāļĢāļĩāļāļķāđāļāđāļ āļāđāļēāļāļāļēāļĢāļāļąāļāļāļĩ āđāļĻāļĢāļĐāļāļĻāļēāļŠāļāļĢāđ āļŠāļāļīāļāļī MIS
- āļ§āļīāļĻāļ§āļāļĢāļĢāļĄāļāļāļĄāļāļīāļ§āđāļāļāļĢāđ āļ§āļīāļāļĒāļēāļāļēāļĢāļāļāļĄāļāļīāļ§āđāļāļāļĢāđ āļāļēāļĢāđāļāļīāļ āļāļĢāļīāļŦāļēāļĢāļāļ§āļēāļĄāđāļŠāļĩāđāļĒāļ āļ§āļīāļĻāļ§āļāļĢāļĢāļĄāļāļēāļĢāđāļāļīāļ āļŦāļĢāļ·āļāļŠāļēāļāļēāļāļĩāđāđāļāļĩāđāļĒāļ§āļāđāļāļ.
- āļĄāļĩāļāļĢāļ°āļŠāļāļāļēāļĢāļāđ āļāđāļēāļāļāļĢāļīāļŦāļēāļĢāļāļ§āļēāļĄāđāļŠāļĩāđāļĒāļāļāđāļēāļāļŠāļīāļāđāļāļ·āđāļ āļŦāļĢāļ·āļāļāļĢāļīāļŦāļēāļĢ Portfolio āļŦāļĢāļ·āļāļĄāļĩāļāļĢāļ°āļŠāļāļāļēāļĢāļāđāļāļĩāđāđāļāļĩāđāļĒāļ§āļāđāļāļ āļāļĒāđāļēāļāļāđāļāļĒ 3 āļāļĩ.
- āļŠāļēāļĄāļēāļĢāļāļ§āļīāđāļāļĢāļēāļ°āļŦāđāļāļ§āļēāļĄāđāļŠāļĩāđāļĒāļāļŦāļĢāļ·āļāļāļĢāļ°āđāļĄāļīāļāļāļ§āļēāļĄāđāļŠāļĩāđāļĒāļāđāļāļ·āđāļāđāļŦāđāļŠāļāļāļāļĨāđāļāļāļāļąāļāļāļļāļāļ āļēāļāļāļāļāļŠāļīāļāđāļāļ·āđāļāđāļĨāļ°āđāļāđāļēāļŦāļĄāļēāļĒāļāļāļāļāļāļāđāļāļĢ.
- āļĄāļĩāļāļ§āļēāļĄāļĢāļđāđāļāđāļēāļāļāļĨāļīāļāļ āļąāļāļāđāļŠāļīāļāđāļāļ·āđāļāļĢāļēāļĒāļĒāđāļāļĒ āđāļĨāļ°āļāļĢāļ°āļāļ§āļāļāļīāļāļēāļĢāļāļēāļŠāļīāļāđāļāļ·āđāļāļĢāļēāļĒāļĒāđāļāļĒ.
- āļŠāļēāļĄāļēāļĢāļāļāļģāđāļŠāļāļāđāļĨāļ°āļŠāļ·āđāļāļŠāļēāļĢāđāļāđāļāļĩ.
- āļŠāļēāļĄāļēāļĢāļāđāļāđāļ āļēāļĐāļēāļāļąāļāļāļĪāļĐāđāļāđāđāļāļĢāļ°āļāļąāļāļāļĩ.
- āļŠāļēāļĄāļēāļĢāļāđāļāđāđāļāļĢāđāļāļĢāļĄ SAS, SQL, MS āđāļāļīāļāļĨāļķāļ āđāļĨāļ° Python āđāļāđāļāļāđāļ.
- āļāđāļēāļāļŠāļēāļĄāļēāļĢāļāļāđāļēāļāđāļĨāļ°āļĻāļķāļāļĐāļēāļāđāļĒāļāļēāļĒāļāļ§āļēāļĄāđāļāđāļāļŠāđāļ§āļāļāļąāļ§āļāļāļāļāļāļēāļāļēāļĢāļāļĢāļļāļāđāļāļĒ āļāļģāļāļąāļ (āļĄāļŦāļēāļāļ) āļāļĩāđ https://krungthai.com/th/content/privacy-policy āļāļąāđāļāļāļĩāđ āļāļāļēāļāļēāļĢāđāļĄāđāļĄāļĩāđāļāļāļāļēāļŦāļĢāļ·āļāļāļ§āļēāļĄāļāļģāđāļāđāļāđāļāđ āļāļĩāđāļāļ°āļāļĢāļ°āļĄāļ§āļĨāļāļĨāļāđāļāļĄāļđāļĨāļŠāđāļ§āļāļāļļāļāļāļĨāļāļĩāđāļĄāļĩāļāļ§āļēāļĄāļāđāļāļāđāļŦāļ§ āļĢāļ§āļĄāļāļķāļāļāđāļāļĄāļđāļĨāļāļĩāđāđāļāļĩāđāļĒāļ§āļāđāļāļāļĻāļēāļŠāļāļēāđāļĨāļ°/āļŦāļĢāļ·āļāļŦāļĄāļđāđāđāļĨāļŦāļīāļ āļāļķāđāļāļāļēāļāļāļĢāļēāļāļāļāļĒāļđāđāđāļāļŠāļģāđāļāļēāļāļąāļāļĢāļāļĢāļ°āļāļģāļāļąāļ§āļāļĢāļ°āļāļēāļāļāļāļāļāļāđāļēāļāđāļāđāļāļĒāđāļēāļāđāļ āļāļąāļāļāļąāđāļ āļāļĢāļļāļāļēāļāļĒāđāļēāļāļąāļāđāļŦāļĨāļāđāļāļāļŠāļēāļĢāđāļāđ āļĢāļ§āļĄāļāļķāļāļŠāļģāđāļāļēāļāļąāļāļĢāļāļĢāļ°āļāļģāļāļąāļ§āļāļĢāļ°āļāļēāļāļ āļŦāļĢāļ·āļāļāļĢāļāļāļāđāļāļĄāļđāļĨāļŠāđāļ§āļāļāļļāļāļāļĨāļāļĩāđāļĄāļĩāļāļ§āļēāļĄāļāđāļāļāđāļŦāļ§āļŦāļĢāļ·āļāļāđāļāļĄāļđāļĨāļāļ·āđāļāđāļ āļāļķāđāļāđāļĄāđāđāļāļĩāđāļĒāļ§āļāđāļāļāļŦāļĢāļ·āļāđāļĄāđāļāļģāđāļāđāļāļŠāļģāļŦāļĢāļąāļāļ§āļąāļāļāļļāļāļĢāļ°āļŠāļāļāđāđāļāļāļēāļĢāļŠāļĄāļąāļāļĢāļāļēāļāđāļ§āđāļāļāđāļ§āđāļāđāļāļāđ āļāļāļāļāļēāļāļāļĩāđ āļāļĢāļļāļāļēāļāļģāđāļāļīāļāļāļēāļĢāđāļŦāđāđāļāđāđāļāļ§āđāļēāđāļāđāļāļģāđāļāļīāļāļāļēāļĢāļĨāļāļāđāļāļĄāļđāļĨāļŠāđāļ§āļāļāļļāļāļāļĨāļāļĩāđāļĄāļĩāļāļ§āļēāļĄāļāđāļāļāđāļŦāļ§ (āļāđāļēāļĄāļĩ) āļāļāļāļāļēāļāđāļĢāļāļđāđāļĄāđāđāļĨāļ°āđāļāļāļŠāļēāļĢāļāļ·āđāļāđāļāļāđāļāļāļāļĩāđāļāļ°āļāļąāļāđāļŦāļĨāļāđāļāļāļŠāļēāļĢāļāļąāļāļāļĨāđāļēāļ§āđāļ§āđāļāļāđāļ§āđāļāđāļāļāđāđāļĨāđāļ§āļāđāļ§āļĒ āļāļąāđāļāļāļĩāđ āļāļāļēāļāļēāļĢāļĄāļĩāļāļ§āļēāļĄāļāļģāđāļāđāļāļāđāļāļāđāļāđāļāļĢāļ§āļāļĢāļ§āļĄāļāđāļāļĄāļđāļĨāļŠāđāļ§āļāļāļļāļāļāļĨāđāļāļĩāđāļĒāļ§āļāļąāļāļāļĢāļ°āļ§āļąāļāļīāļāļēāļāļāļēāļāļĢāļĢāļĄāļāļāļāļāđāļēāļāđāļāļ·āđāļāļāļĢāļĢāļĨāļļāļ§āļąāļāļāļļāļāļĢāļ°āļŠāļāļāđāđāļāļāļēāļĢāļāļīāļāļēāļĢāļāļēāļĢāļąāļāļāļļāļāļāļĨāđāļāđāļēāļāļģāļāļēāļ āļŦāļĢāļ·āļāļāļēāļĢāļāļĢāļ§āļāļŠāļāļāļāļļāļāļŠāļĄāļāļąāļāļī āļĨāļąāļāļĐāļāļ°āļāđāļāļāļŦāđāļēāļĄ āļŦāļĢāļ·āļāļāļīāļāļēāļĢāļāļēāļāļ§āļēāļĄāđāļŦāļĄāļēāļ°āļŠāļĄāļāļāļāļāļļāļāļāļĨāļāļĩāđāļāļ°āđāļŦāđāļāļģāļĢāļāļāļģāđāļŦāļāđāļ āļāļķāđāļāļāļēāļĢāđāļŦāđāļāļ§āļēāļĄāļĒāļīāļāļĒāļāļĄāđāļāļ·āđāļāđāļāđāļāļĢāļ§āļāļĢāļ§āļĄ āđāļāđ āļŦāļĢāļ·āļāđāļāļīāļāđāļāļĒāļāđāļāļĄāļđāļĨāļŠāđāļ§āļāļāļļāļāļāļĨāđāļāļĩāđāļĒāļ§āļāļąāļāļāļĢāļ°āļ§āļąāļāļīāļāļēāļāļāļēāļāļĢāļĢāļĄāļāļāļāļāđāļēāļāļĄāļĩāļāļ§āļēāļĄāļāļģāđāļāđāļāļŠāļģāļŦāļĢāļąāļāļāļēāļĢāđāļāđāļēāļāļģāļŠāļąāļāļāļēāđāļĨāļ°āļāļēāļĢāđāļāđāļĢāļąāļāļāļēāļĢāļāļīāļāļēāļĢāļāļēāļāļēāļĄāļ§āļąāļāļāļļāļāļĢāļ°āļŠāļāļāđāļāļąāļāļāļĨāđāļēāļ§āļāđāļēāļāļāđāļ āđāļāļāļĢāļāļĩāļāļĩāđāļāđāļēāļāđāļĄāđāđāļŦāđāļāļ§āļēāļĄāļĒāļīāļāļĒāļāļĄāđāļāļāļēāļĢāđāļāđāļāļĢāļ§āļāļĢāļ§āļĄ āđāļāđ āļŦāļĢāļ·āļāđāļāļīāļāđāļāļĒāļāđāļāļĄāļđāļĨāļŠāđāļ§āļāļāļļāļāļāļĨāđāļāļĩāđāļĒāļ§āļāļąāļāļāļĢāļ°āļ§āļąāļāļīāļāļēāļāļāļēāļāļĢāļĢāļĄ āļŦāļĢāļ·āļāļĄāļĩāļāļēāļĢāļāļāļāļāļ§āļēāļĄāļĒāļīāļāļĒāļāļĄāđāļāļ āļēāļĒāļŦāļĨāļąāļ āļāļāļēāļāļēāļĢāļāļēāļāđāļĄāđāļŠāļēāļĄāļēāļĢāļāļāļģāđāļāļīāļāļāļēāļĢāđāļāļ·āđāļāļāļĢāļĢāļĨāļļāļ§āļąāļāļāļļāļāļĢāļ°āļŠāļāļāđāļāļąāļāļāļĨāđāļēāļ§āļāđāļēāļāļāđāļāđāļāđ āđāļĨāļ°āļāļēāļ āļāļģāđāļŦāđāļāđāļēāļāļŠāļđāļāđāļŠāļĩāļĒāđāļāļāļēāļŠāđāļāļāļēāļĢāđāļāđāļĢāļąāļāļāļēāļĢāļāļīāļāļēāļĢāļāļēāļĢāļąāļāđāļāđāļēāļāļģāļāļēāļāļāļąāļāļāļāļēāļāļēāļĢ".
āļāļĢāļ°āļŠāļāļāļēāļĢāļāđ:
6 āļāļĩāļāļķāđāļāđāļ
āļāļąāļāļĐāļ°:
Instrument, Excel, SAS, English
āļāļĢāļ°āđāļ āļāļāļēāļ:
āļāļēāļāļāļĢāļ°āļāļģ
āđāļāļīāļāđāļāļ·āļāļ:
āļŠāļēāļĄāļēāļĢāļāļāđāļāļĢāļāļāđāļāđ
- Credit Risk Modelling - IFRS 9 model development, validation, Basel II/III solutions, including RWA optimisation, scorecard development, and PD/LGD/EAD model development.
- Market, Liquidity and Operational Risk - calculation of market, liquidity and operational risk capital under various regulations, assisting with implementation, and organisational review.
- Risk management advice: reviewing the current risk management framework, and designi ...
- Complex financial instrument valuation: assisting you in financial instrument valuation in order to evaluate its fair valuation in order to evaluate its fair value.
- Insurance modelling: developing and validating risk management models for insurers including liability.
- Conduct financial risk models design and development, model validation and testing, and other advanced data analytics on a wide range of client portfolios (financial and non-financial services).
- Develop and apply credit risk methodologies including IFRS 9 and Basel II /III PD/LGD/EAD models etc.
- Analyse and interpret quantitative results to understand business impact.
- H andle and manage work streams, build relationships and manage clients during the implementation of projects.
- Communicate confidently in a clear, concise and articulate manner - verbally and in written form.
- Seek opportunities to learn about other cultures and other parts of the business across the Network of PwC firms.
- Uphold the firm s code of ethics and business conduct.
- Preferred skills.
- Experience in current financial regular landscape will be an advantage (Basel II /III, IFRS 9 etc.).
- Proficient in Excel and/or other analytics platforms (e.g. SAS, SQL, R, Python, Excel VBA).
- Excellent English and Thai written and verbal communication skills.
- Demonstrate strong inter-personal skills and good communication skills, including the ability to document reports and conduct presentations for clients and key stakeholders.
- University degree in a quantitative discipline (e.g. Mathematical Science, Financial Engineering, Actuarial, Statistics etc.).
- Analytical and independent thinker with strong English and Thai written and verbal communication skills.
- Between 3 and 6 years of relevant experience.
- If you have any questions, please feel free to contact Prangnart, Human Resources Team, on [email protected].
- We thank all applicants. Please note that only short-listed candidates will be contacted for interviews.
- Education (if blank, degree and/or field of study not specified).
- Degrees/Field of Study required: Bachelor Degree Degrees/Field of Study preferred:Certifications (if blank, certifications not specified).
- Required Skills.
- Optional Skills.
- Desired Languages (If blank, desired languages not specified).
- Travel Requirements.
- Not Specified
- Available for Work Visa Sponsorship?.
- Yes
- Government Clearance Required?.
- No
āļāļĢāļ°āļŠāļāļāļēāļĢāļāđ:
5 āļāļĩāļāļķāđāļāđāļ
āļāļąāļāļĐāļ°:
Data Analysis, Automation, Python
āļāļĢāļ°āđāļ āļāļāļēāļ:
āļāļēāļāļāļĢāļ°āļāļģ
āđāļāļīāļāđāļāļ·āļāļ:
āļŠāļēāļĄāļēāļĢāļāļāđāļāļĢāļāļāđāļāđ
- Work with stakeholders throughout the organization to understand data needs, identify issues or opportunities for leveraging company data to propose solutions for support decision making to drive business solutions.
- Adopting new technology, techniques, and methods such as machine learning or statistical techniques to produce new solutions to problems.
- Conducts advanced data analysis and create the appropriate algorithm to solve analytics problems.
- Improve scalability, stability, accuracy, speed, and efficiency of existing data model.
- Collaborate with internal team and partner to scale up development to production.
- Maintain and fine tune existing analytic model in order to ensure model accuracy.
- Support the enhancement and accuracy of predictive automation capabilities based on valuable internal and external data and on established objectives for Machine Learning competencies.
- Apply algorithms to generate accurate predictions and resolve dataset issues as they arise.
- Be Project manager for Data project and manager project scope, timeline, and budget.
- Manage relationships with stakeholders and coordinate work between different parties as well as providing regular update.
- Control / manage / govern Level 2 support, identify, fix and configuration related problems.
- Keep maintaining/up to date of data modelling and training model etc.
- Run through Data flow diagram for model development.
- EDUCATION.
- Bachelor's degree or higher in computer science, statistics, or operations research or related technical discipline.
- EXPERIENCE.
- At least 5 years experience in a statistical and/or data science role optimization, data visualization, pattern recognition, cluster analysis and segmentation analysis, Expertise in advanced Analytica l techniques such as descriptive statistical modelling and algorithms, machine learning algorithms, optimization, data visualization, pattern recognition, cluster analysis and segmentation analysis.
- Expertise in advanced analytical techniques such as descriptive statistical modelling and algorithms, machine learning algorithms, optimization, data visualization, pattern recognition, cluster analysis and segmentation analysis.
- Experience using analytical tools and languages such as Python, R, SAS, Java, C, C++, C#, Matlab, SPSS IBM, Tableau, Qlikview, Rapid Miner, Apache, Pig, Spotfire, Micro S, SAP HANA, Oracle, or SOL-like languages.
- Experience working with large data sets, simulation/optimization and distributed computing tools (e.g., Map/Reduce, Hadoop, Hive, Spark).
- Experience developing and deploying machine learning model in production environment.
- Knowledge in oil and gas business processes is preferrable.
- OTHER REQUIREMENTS.
āļāļĢāļ°āļŠāļāļāļēāļĢāļāđ:
5 āļāļĩāļāļķāđāļāđāļ
āļāļąāļāļĐāļ°:
Statistics, Finance, Risk Management
āļāļĢāļ°āđāļ āļāļāļēāļ:
āļāļēāļāļāļĢāļ°āļāļģ
āđāļāļīāļāđāļāļ·āļāļ:
āļŠāļēāļĄāļēāļĢāļāļāđāļāļĢāļāļāđāļāđ
- Bachelor s degree (or equivalent) degree in a quantitative field such as Data Science, Actuarial Science, Statistics, or Mathematics.
- 5+ years of related practical experience, preferably in commercial insurance sector.
- Solid understanding of insurance pricing principles, loss reserving, and risk assessment methodologies.
- Familiarity with insurance industry regulations, standards, and best practices.
- Develop and maintain loss cost models using GLMs and other advanced statistical techniques, incorporating relevant variables and factors for accurate pricing and risk assessment.
- Analyse historical insurance data to identify patterns and trends, and determine the impact of various factors on loss costs.
- Collaborate with underwriting, claims, and finance teams to understand business needs and provide data-driven insights for portfolio management.
- Conduct rate level reviews to ensure appropriate pricing of insurance products, considering risk exposure, market dynamics, and profitability goals.
- Enhance loss cost models over time by incorporating new data sources, refining variables,.
- and exploring innovative modelling techniques.
- Evaluate the impact of pricing strategies, policy changes, and market shifts on portfolio performance, and make recommendations for adjustments, if needed.
- Present findings and recommendations to stakeholders, including senior management and underwriting teams, in clear and concise reports.
- Work closely with other departments including Underwriting, Actuarial, and Risk Management, providing them with the data and insights needed to make evidence-based decisions.
- Functional Competency.
- Excellent analytical and problem-solving skills, with the ability to translate data into meaningful insights and recommendations.
- Strong communication skills to effectively convey complex findings and recommendations to both technical and non-technical stakeholders.
- Attention to detail and ability to work independently, managing multiple projects and deadlines efficiently.
- Strong proficiency in statistical modeling techniques, specifically GLMs, and experience with software tools like R, SAS, or Python.
- Proficiency with data analysis and visualisation tools and platforms, preferably Qliksense, Power BI, Alteryx, etc.
- Educational.
- Bachelor s degree (or equivalent) degree in a quantitative field such as Data Science, Actuarial Science, Statistics, or Mathematics.
- 5+ years of related practical experience, preferably in commercial insurance sector.
- Solid understanding of insurance pricing principles, loss reserving, and risk assessment methodologies.
- Familiarity with insurance industry regulations, standards, and best practices.
- Develop and maintain loss cost models using GLMs and other advanced statistical techniques, incorporating relevant variables and factors for accurate pricing and risk assessment.
- Analyse historical insurance data to identify patterns and trends, and determine the impact of various factors on loss costs.
- Collaborate with underwriting, claims, and finance teams to understand business needs and provide data-driven insights for portfolio management.
- Conduct rate level reviews to ensure appropriate pricing of insurance products, considering risk exposure, market dynamics, and profitability goals.
- Enhance loss cost models over time by incorporating new data sources, refining variables,.
- and exploring innovative modelling techniques.
- Evaluate the impact of pricing strategies, policy changes, and market shifts on portfolio performance, and make recommendations for adjustments, if needed.
- Present findings and recommendations to stakeholders, including senior management and underwriting teams, in clear and concise reports.
- Work closely with other departments including Underwriting, Actuarial, and Risk Management, providing them with the data and insights needed to make evidence-based decisions.
- Functional Competency.
- Excellent analytical and problem-solving skills, with the ability to translate data into meaningful insights and recommendations.
- Strong communication skills to effectively convey complex findings and recommendations to both technical and non-technical stakeholders.
- Attention to detail and ability to work independently, managing multiple projects and deadlines efficiently.
- Strong proficiency in statistical modeling techniques, specifically GLMs, and experience with software tools like R, SAS, or Python.
- Proficiency with data analysis and visualisation tools and platforms, preferably Qliksense, Power BI, Alteryx, etc.
āļāļĢāļ°āļŠāļāļāļēāļĢāļāđ:
8 āļāļĩāļāļķāđāļāđāļ
āļāļąāļāļĐāļ°:
Research, Risk Management, Statistics, English
āļāļĢāļ°āđāļ āļāļāļēāļ:
āļāļēāļāļāļĢāļ°āļāļģ
āđāļāļīāļāđāļāļ·āļāļ:
āļŠāļēāļĄāļēāļĢāļāļāđāļāļĢāļāļāđāļāđ
- Develop, maintain, and calibrate existing quantitative risk models, including provisioning models and credit scoring tailored to various portfolio types and financial institutions.
- Perform both conceptual and quantitative reviews of models, including validation, using programming scripts or automated tools.
- Provide business insights on post-model adjustments, such as management overlays.
- Research risk management topics and stay updated on recent industry developments.
- Prepare comprehensive model documentation, reports, or presentations to communicate methodologies and results to clients.
- Effectively convey observations, results, thoughts, and initiatives to clients through proficient presentation during virtual and in-person meetings as needed.
- Propose innovative ideas to enhance team efficiency and effectiveness.
- Collaborate with colleagues and clients across multiple countries, primarily within Southeast Asia.
- Support partners and directors in preparing client proposals under tight deadlines.
- Mentor and onboard junior staff, ensuring the delivery of high-quality work.
- You will be expected to communicate closely with senior management and client personnel; assist in proposal development; mentor and develop junior team members; and maintain up-to-date knowledge of financial risk management methodologies, current corporate governance and regulatory developments/requirements, both locally and internationally
- Your role as a leader:At Deloitte, we believe in the importance of empowering our people to be leaders at all levels. We connect our purpose and shared values to identify issues as well as to make an impact that matters to our clients, people and the communities. Additionally, Managers across our Firm are expected to:Actively seek out developmental opportunities for growth, act as strong brand ambassadors for the firm as well as share their knowledge and experience with others.
- Respect the needs of their colleagues and build up cooperative relationships.
- Understand the goals of our internal and external stakeholder to set personal priorities as well as align their teams work to achieve the objectives.
- Constantly challenge themselves, collaborate with others to deliver on tasks and take accountability for the results.
- Build productive relationships and communicate effectively in order to positively influence teams and other stakeholders.
- Offer insights based on a solid understanding of what makes Deloitte successful.
- Project integrity and confidence while motivating others through team collaboration as well as recognising individual strengths, differences, and contributions.
- Understand disruptive trends and promote potential opportunities for improvement.
- You are someone with:A degree, preferably in technical engineering, statistics, economics, mathematics, finance, accountancy, or a related field.
- Possess a minimum of 5 to 8 years of relevant work experience. A background in banking or financial institutions is preferred, but this can be supplemented with significant knowledge of the financial markets and banking industry.
- Strong knowledge of risk management, with a focus on one of the risk domains namely credit risk, market risk, operational risk and climate risk preferred.
- Ability to work independently and collaboratively with a diverse range of staff on qualitative and quantitative risk management in multitasking and cross-country settings.
- Proficient in data analytics or statistical analysis tools (i.e., Python and SAS), with advanced Excel skills.
- Experience in mentoring and coaching at least 2-3 junior team members.
- Proficient in business-level English, with the ability to communicate ideas and prepare professional client presentations.
- Due to volume of applications, we regret only shortlisted candidates will be notified.
- Please note that Deloitte will never reach out to you directly via messaging platforms to offer you employment opportunities or request for money or your personal information. Kindly apply for roles that you are interested in via this official Deloitte website.Requisition ID: 107227In Thailand, the services are provided by Deloitte Touche Tohmatsu Jaiyos Co., Ltd. and other related entities in Thailand ("Deloitte in Thailand"), which are affiliates of Deloitte Southeast Asia Ltd. Deloitte Southeast Asia Ltd is a member firm of Deloitte Touche Tohmatsu Limited. Deloitte in Thailand, which is within the Deloitte Network, is the entity that is providing this Website.
āļāļąāļāļĐāļ°:
Statistics, SQL, Excel
āļāļĢāļ°āđāļ āļāļāļēāļ:
āļāļēāļāļāļĢāļ°āļāļģ
āđāļāļīāļāđāļāļ·āļāļ:
āļŠāļēāļĄāļēāļĢāļāļāđāļāļĢāļāļāđāļāđ
- Interpret data, analyze results that using statistical techniques and provide ongoing reports.
- Develop and implement database, data collection systems, data analytics and other strategies that optimize statistical efficiency and quality.
- Acquire data from primary or secondary data sources and maintain databases/data systems.
- Create presentations and reports based on recommendations and findings by using graphs, infographics, and other methods to visualize data.
- Qualifications Bachelor s degree or higher in Business Analytics, Computer Science, Engineering, Statistics, or related fields.
- Proven professional experience in retail banking, consumer products, or related banking functions as a Data Analyst or Business Analyst.
- Strong knowledge and experience in SQL, Excel, Power BI, Tableau, QlikView, Business Objects, Python, R, SAS, or other business intelligence tools.
- Strong analytical skills with the ability to collect, organize, analyze, and disseminate significant amounts of information with attention to detail and accuracy.
- Adept at queries, report writing and presenting findings.
- Interested candidate, please submit your CV to [email protected] We're committed to bringing passion and customer focus to the business. If you like wild growth and working with happy, enthusiastic over-achievers, you'll enjoy your career with us.
āļāļĢāļ°āļŠāļāļāļēāļĢāļāđ:
3 āļāļĩāļāļķāđāļāđāļ
āļāļąāļāļĐāļ°:
English
āļāļĢāļ°āđāļ āļāļāļēāļ:
āļāļēāļāļāļĢāļ°āļāļģ
āđāļāļīāļāđāļāļ·āļāļ:
āļŋ20,000+
- Ensure that appropriate processes, policies and procedures are in place that meets Basel - Pillar 1,2,3 requirements imposed by regulators.
- Evaluate impact of changes in regulations, strategy or IRB models on capital requirement and profitability (revenue, credit loss, and operation cost) of the portfolio.
- Advice Business Units on credit capital treatment for new products and/or structured deal.
- Collaborate with CIMB Group and Business Units of CIMB Thai to set Risk Appetite Statement, Risk Posture, Risk Appetite and seek approval from respective management/committees.
- Collaborate and co-ordinate with value chain to improve efficiency of capital usage - various options in calculating of Risk Weighted Assets (RWA) to achieve optimal benefit for the bank and financial group in terms of capital management and treatment and propose initiatives with impact analysis to respective management / committees.
- Perform analysis on Risk Weighted Assets (RWA) and Capital adequacy ratios to be submitted to BOT and BNM. Monitor, analyze and report movement of Risk Weighted Assets (RWA) and Capital adequacy ratios.
- Collaborate with Data Management to do SIT / UAT to ensure data transferred to CIMB Group for RWA calculation at Group level meet Bank Negara of Malaysia (BNM) requirements and produce the RWA result correctly.
- Be Risk Management s representative to attend the meeting held by Bank of Thailand or Basel Club (Basel Working Group of all Thai Commercial Banks) in order to address, provide impact analysis of the changes/ new Basel requirements with respect to regulatory and capital risks and report progress and/or issues to relevant parties and respective management / committees as required.
- Co-ordinate and engage various departments for information and input to prepare ICAAP/Pillar 3 disclosure report and seek approval on ICAAP report from Board of Directors prior to submission to BOT within timeline.
- Collborate with Data Management team to investigate issues on data used in the calculation of RWA and capital ratios.
- Collaborate with Sustainability Team and Climate Risk Team to ensure that all assumptions (e.g. climate scenarios) and Climate stress testing methodology meet BOT requirements.
- Work with finance to prepare Basel Reports for submission to regulators.
- Perform any other duties as and when assigned.
- Bachelor degree or higher in a numerate discipline including finance, banking, risk management.
- Certified CFA or FRM (preferred).
- At least 10 years related experience e.g. credit risk data analytics, portfolio management, capital adequacy analysis, profit-loss analysis, RaROC, RWA and ECL calculation.
- at least 3 years of experience in ICAAP reporting, credit RWA calculation (SA and IRB), and stress testing.
- At least 2-year experience in data analysis, database management, MIS, or/and dashboard design and management is a plus.
- Quantitative skills e.g. data analytics, RWA calculation and analysis.
- Critical and creative thinking skills.
- Change management skills e.g. adaptability to change, collaboration and strategic planning.
- Database management, MIS, and dashboard design and management.
- Competency with BI and presentation tools e.g. Tableau.
- SAS, SQL, Python or other programming language is a plus.
- Knowledge on financial statement analysis and regulatory requirements imposed by BOT, BNM and Basel.
- Remark: The Bank requires the verification of criminal records prior consideration for employment to ensure secured and maintain standards of the organization.
āļāļĢāļ°āļŠāļāļāļēāļĢāļāđ:
5 āļāļĩāļāļķāđāļāđāļ
āļāļąāļāļĐāļ°:
Data Analysis, English
āļāļĢāļ°āđāļ āļāļāļēāļ:
āļāļēāļāļāļĢāļ°āļāļģ
- Deep dive analysis and monitoring credit performance of new acquisition by analyzing first year default cases, trigger actions if vintage reaches critical levels. Coordinate all parties to provide mitigation steps.
- Revise underwriting standard to manage default rate within MOB12 to be acceptable ratio (products are profitable).
- Quality check when account is default in the first year. To investigate and analyze root cause of credit risk default and to recommend improving credit process.
- Summarize key findings and report it to the Team Head of Retail Credit Policy & Portfolio Management for further actions.
- Perform analysis to find opportunity on selective segment with acceptable risk level and prepare underwriting standard according to initiatives. Plan and perform A-B testing of different underwriting policies in combination with credit scoring, provide comparative study of the champion challenger approach. Closely monitor initiatives/ test programs and adjust underwriting rules if it requires.
- Plan and manage risk related change requests in the approval process, organize UAT and coordinate amongst the different stakeholders.
- Act as an expert of data interpretation, perform data investigation related to the portfolio management, help the analytics team by liaising with CRI or Datawarehouse in defining new or changed fields, data structuring and definition.
- Manage the development of the credit risk data self-service platform by drafting requirements and approving results.
- Continuously improving reporting ability by actively coming up with aspects and dimensions that are to be monitored.
- Regularly provide comprehensive and high-quality portfolio risk measurement, analysis and reporting on retail segment to Senior Managements and Committees within target dates or timelines to take the right strategic decisions on timely manners through deep-dive analysis.
- Provide recommendation according to deep dive analysis for loss mitigation.
- Support all portfolio management strategy & associated risk reward optimization initiatives across Acquisition, Account management & debt collection & recovery functions.
- Bachelor s degree or Higher in Finance & Banking, Economics, Business Administration, Engineering.
- At least total 5 years experienced in retail credit policy, portfolio monitoring, retail risk management, Finance.
- Strong computer skills required, proficiency in Excel, SAS enterprise guide, Power BI.
- Demonstrated aptitude for analytics and exceptional problem-solving skills.
- Ability to communicate complex ideas effectively - both verbally and in writing - in English.
āļāļąāļāļĐāļ°:
Java, J2EE, JSP
āļāļĢāļ°āđāļ āļāļāļēāļ:
āļāļēāļāļāļĢāļ°āļāļģ
āđāļāļīāļāđāļāļ·āļāļ:
āļŠāļēāļĄāļēāļĢāļāļāđāļāļĢāļāļāđāļāđ
- Develop and maintenance of pragmatic high quality software to support business requirement.
- Work with technology team to build a maintainable technology infrastructure including build & testing environment.
- Contribute to designing and building production systems on Client/Server and web application.
- Compose system design documents such as database diagram, user manual.
- Execute Unit Test and System Integration Test.
- Cooperate with other teams to work with their backend systems.
- Bachelor s degree or higher in Computer Engineering, Computer Science, Information Technology, related field.
- At least 0 -5 years experience in system development.
- Can work as a team, must be able to work under extremely high pressure, excellent communications and interpersonal skills.
- Computer Language - web application PA,.Net, C, C#.Net, Java, J2EE, JSP, JavaScript, Java Servlet, Spring, Hibernate, EJB, Strut, Shell Script, PL/SQL, OOP, Android & IOS.
- OS & Database - Oracle, Teradata, Greenplum, Hadoop, Unix, Linux, MySQL, SQL Command/Server, Tunning and Data Stage.
- Reporting Tool - Oracle OBIEE, SAS VA, Tableau, Cognos, Crystal Report.
āļāļąāļāļĐāļ°:
Statistics, Research, Finance
āļāļĢāļ°āđāļ āļāļāļēāļ:
āļāļēāļāļāļĢāļ°āļāļģ
āđāļāļīāļāđāļāļ·āļāļ:
āļŠāļēāļĄāļēāļĢāļāļāđāļāļĢāļāļāđāļāđ
- Engage stakeholders to understand business problems and customize analytic and predictive model solutions to business needs.
- Solve problems using advanced statistical techniques including, but not limited to regression/logic regression, bootstrap, factor analysis, decision tree, clustering & binning, and basket analysis.
- Conduct exploratory data profiling techniques, common summary descriptive statistics, etc.
- Develop and maintain expertise in a wide range of new technologies, methodologies, and techniques facilitating advanced research, decision sciences, and systems engineering.
- Support the design and management of market and product experiments and pilots to test hypotheses or generate test and control observation data.
- Partner with Technology, Product Management, Engineering, Marketing, Sales, and Finance teams to deliver solutions for clients and into the operation.
- Build and lead a team of analysts, while initially serving as an individual contributor.
- Lead projects with moderate complexity.
- Engage internal customers to understand problems and customize analytic and predictive model solutions to business needs.
- Bachelor's or Master's in Mathematics, Science, Statistics or related Technical field; or Equivalent related professional experience (e.g. driving significant and sustained change and performance improvement from data-driven insights).
- Demonstrates statistical competency and requires limited supervision.
- Familiar with SQL, Python, or R, or any other major data analysis programming language.
- Experience working in the fundamentals of big data architecture (streaming events, data lakes, analytics engines) and relational database models.
- Display strong domain knowledge, business acumen, and critical reasoning skills.
- Strong skills in Excel, PowerPoint, and statistical/data processing software packages and programming languages (e.g. SAS, R, SQL, SPSS, etc.).
- Mines data sets using sophisticated analytical techniques to generate insights and inform business decisions.
- Identifies and tests hypotheses, ensuring statistical significance through experimental design and builds predictive models for business application, product development, etc.
- Translates quantitative analyses and findings into accessible visuals for all stakeholders and multiple audiences, and provide clear view into interpreting data.
- Ability to work in and among cross functional teams.
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āļāļģāđāļāļ°āļāļģāļāļēāļĢāļŦāļēāļāļēāļāđāļāļīāļāđāļāļĨāļŠāļļāļāļĒāļāļ 50 āļāļĢāļīāļĐāļąāļāļāļĩāđāļāļāļĢāļļāđāļāđāļŦāļĄāđāļāļĒāļēāļāļĢāđāļ§āļĄāļāļēāļāļāđāļ§āļĒāļĄāļēāļāļāļĩāđāļŠāļļāļ 2025
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