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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°āđāļ āļāļāļēāļ:
āļāļēāļāļāļĢāļ°āļāļģ
āđāļāļīāļāđāļāļ·āļāļ:
āļŠāļēāļĄāļēāļĢāļāļāđāļāļĢāļāļāđāļāđ
- About TikTokTikTok is the leading destination for short-form mobile video. At TikTok, our mission is to inspire creativity and bring joy. TikTok's global headquarters are in Los Angeles and Singapore, and its offices include New York, London, Dublin, Paris, Berlin, Dubai, Jakarta, Seoul, and Tokyo. Why Join UsCreation is the core of TikTok's purpose. Our products are built to help imaginations thrive. This is doubly true of the teams that make our innovations possible. Together, we inspire creativity and enrich life - a mission we aim towards achieving every day. To us, every chall ...
- Minimum Qualifications-Bachelor degree or above, graduate degree is preferred, math, statistics, computer-related majors are preferred, strong capability of data analytics is required;-experience in research and analysis positions in consulting, e-commerce/ads is preferred. Preferred Qualifications-Outstanding reasoning capability and logic, with good problem solving methodologies, being able to independently output quality strategic solutions, strong learning and adaptive ability, strategic framework thinking, sensitivity and insight into the business;-Have some strategic thinking capacity, able to quickly understand the business and discover the connection between business and data performance;-Proficiency in SQL for data analysis, understanding of common statistical and analytical methods, and skills in BI visualization tools such as Tableau/Power BI is a plus;-Fast learner, passionate about participating in business, and able to adapt to high intensity and fast growth work environment.TikTok is committed to creating an inclusive space where employees are valued for their skills, experiences, and unique perspectives. Our platform connects people from across the globe and so does our workplace. At TikTok, our mission is to inspire creativity and bring joy. To achieve that goal, we are committed to celebrating our diverse voices and to creating an environment that reflects the many communities we reach. We are passionate about this and hope you are too.By submitting an application for this role, you accept and agree to our global applicant privacy policy, which may be accessed here: https://careers.tiktok.com/legal/privacy.If you have any questions, please reach out to us at [email protected].
āļāļĢāļ°āđāļ āļāļāļēāļ:
āļāļēāļāļāļĢāļ°āļāļģ
āđāļāļīāļāđāļāļ·āļāļ:
āļŋ50,000 - āļŋ70,000, āļŠāļēāļĄāļēāļĢāļāļāđāļāļĢāļāļāđāļāđ
- Lazada s strong performance led to the acquisition of a majority stake by Alibaba Group in April 2016. Given our tremendous growth, we are searching for dynamic, entrepreneurial, broad-minded individuals to be part of our rapidly expanding team! Join our diverse and motivated team to hone in on your creativity as well as implement new initiatives within a nurturing, equal opportunity environment!.
- Lazada Thailand employs over 850+ professionals and has won many awards namely for Top Marketplace from Priceza and People's Choice 2017..
- Job Scope.
- ę· Analyze and interpret complex data sets to uncover insights and trends that drive business strategy and decision making.
- ę· Data extraction via SQL for analysis and reporting purpose, ensuring data quality and accuracy.
- ę· Design and maintain dashboards, reports, and visualizations using tools to communicate insights effectively.
- ę· Participate in data governance initiatives, ensuring compliance with data privacy and security regulations.
- ę· Collaborate with cross-functional teams to consolidate and track progress of business actions, as well as ensuring delivery of reports and insights.
- ę· Maintain in-house data and documentation portals, ensuring data quality and up-to-date information & reports.
- ę· Stay updated with industry trends and new technologies to enhance data analytics capabilities..
- ę· Bachelor's/Master's Degree, preferably in MBA, Management, Business Analytics, Statistics, Mathematics.
- ę· Strong analytical and problem-solving skills, with ability to breakdown complex problems into simpler pieces.
- ę· Effective communicator with excellent presentation skills.
- ę· Self-motivated, strong ownership and strong team management skills.
- ę· Prior knowledge & experience on working with SQL is desirable, and at least one data visualization tool (e.g., Tableau, Power BI).
- ę· At least 2-3 years of work experience in analytics, consulting, or other quantitative position would be highly preferred.
āļāļąāļāļĐāļ°:
Excel
āļāļĢāļ°āđāļ āļāļāļēāļ:
āļāļēāļāļāļĢāļ°āļāļģ
āđāļāļīāļāđāļāļ·āļāļ:
āļŠāļēāļĄāļēāļĢāļāļāđāļāļĢāļāļāđāļāđ
- Conceptualizing and executing trade promotions and rolling out impactful in-store campaigns in collaboration with marketing and sales teams.
- Developing and implementing trade marketing strategies to increase brand visibility and drive sales.
- Collaborating with sales teams to identify market trends and opportunities for product promotion.
- Creating and executing trade marketing campaigns and promotions to support product launches and achieve sales targets.
- Monitoring and analyzing sales data to evaluate the effectiveness of trade marketing initiatives.
- Building and maintaining strong relationships with key trade partners and distributors.
- Coordinating with cross-functional teams, including marketing, sales, and supply chain, to ensure seamless execution of trade marketing activities.
- Analyzing and recommending brands/channel combinations to maximize sell-out and profitability..
- Bachelor's degree in marketing, business administration, or a related field.
- Previous experience in trade marketing or sales, preferably in the consumer goods industry.
- Strong analytical skills and ability to interpret market data and trends.
- Proficiency in Microsoft Excel.
āļāļĢāļ°āđāļ āļāļāļēāļ:
āļāļēāļāļāļĢāļ°āļāļģ
āđāļāļīāļāđāļāļ·āļāļ:
āļŠāļēāļĄāļēāļĢāļāļāđāļāļĢāļāļāđāļāđ
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- āļāļąāļāļāļģāđāļĨāļ°āļ§āļīāđāļāļĢāļēāļ°āļŦāđāļĢāļēāļĒāļāļēāļāļāļĩāđāđāļāļĩāđāļĒāļ§āļāđāļāļ āđāļāđāļ āļĢāļēāļĒāļāļēāļāļāļēāļĢāļāļąāļāđāļāļĢāļāļāļēāļĢāļāđāļē āļĢāļēāļĒāļāļēāļāļĨāļđāļāļŦāļāļĩāđāļāđāļēāļāļāļģāļĢāļ°
- āļāļģāļŦāļāđāļēāļāļĩāđāđāļāđāļāđāļĨāļāļēāļāļāļāļāļāļ°āļāļāļļāļāļĢāļĢāļĄāļāļēāļĢāļŠāļīāļāđāļāļ·āđāļ āļĢāļ§āļĄāļāļķāļāļāļēāļĢāļāļąāļāļāļēāļĢāļāļĢāļ°āļāļļāļĄ āļāļēāļĢāļāļąāļāđāļāļĢāļĩāļĒāļĄāđāļāļāļŠāļēāļĢāļāļĢāļ°āļāļāļāļāļēāļĢāļāļĢāļ°āļāļļāļĄ āđāļĨāļ°āļāļēāļĢāļāļąāļāđāļāļĢāļĩāļĒāļĄāļĢāļēāļĒāļāļēāļāļāļēāļĢāļāļĢāļ°āļāļļāļĄ
- āļāļđāđāļĨ āļāļĢāļ§āļāļŠāļāļāļŦāļĨāļąāļāļāļĢāļ°āļāļąāļ āđāļāđāļ āđāļāļīāļāļŠāļ āļŦāļāļąāļāļŠāļ·āļāļāđāļģāļāļĢāļ°āļāļąāļ (BG) āđāļŦāđāļāļđāļāļāđāļāļāļāļēāļĄāļāđāļāļāļģāļŦāļāļāļāļāļāļāļĢāļīāļĐāļąāļāļŊ āđāļĨāļ°āļĄāļĩāļāļĨāļāļąāļāļāļąāļāđāļāđāļāļēāļĄāļāļāļŦāļĄāļēāļĒ āļāļĢāđāļāļĄāļāļąāđāļāļāļēāļĢāđāļŦāđāļāļĨāļāļāļāđāļāļāļāļĢāļāļĩāļŦāļĨāļąāļāļāļĢāļ°āļāļąāļāđāļāđāļāđāļāļīāļāļŠāļ āđāļāļ·āđāļāļĨāļāļāļ§āļēāļĄāđāļŠāļĩāđāļĒāļāđāļāļāļēāļĢāđāļĢāļĩāļĒāļāđāļāđāļāđāļāļīāļāđāļĄāđāđāļāđ
- āļāļĢāļ°āđāļĄāļīāļāļāđāļēāđāļāļ·āđāļāļŦāļāļĩāđāļŠāļāļŠāļąāļĒāļāļ°āļŠāļđāļāļĢāļēāļĒāđāļāļĢāļĄāļēāļŠ āđāļĨāļ°āļĢāļēāļĒāļāļĩ āļŠāļģāļŦāļĢāļąāļāļāļąāļāļāļģāļāļāļāļēāļĢāđāļāļīāļ
- āļāļĢāļ°āļŠāļēāļāļāļēāļāđāļĨāļ°āđāļŦāđāļāđāļāļĄāļđāļĨāļāļąāļāļāļĢāļīāļĐāļąāļāļāļĢāļ°āļāļąāļāļ āļąāļĒ (Trade Credit Insurance) āđāļāļāļēāļĢāļāļĢāļ°āđāļĄāļīāļāļ§āļāđāļāļīāļāđāļŦāđāļŠāļīāļāđāļāļ·āđāļāļāļąāļāļĨāļđāļāļāđāļē
- āļāļĢāļīāļāļāļēāļāļĢāļĩ/āđāļ āļŠāļēāļāļēāļāļēāļĢāđāļāļīāļ āļāļąāļāļāļĩ āđāļĻāļĢāļĐāļāļĻāļēāļŠāļāļĢāđ āļŦāļĢāļ·āļ āļŠāļēāļāļēāļāļ·āđāļāļāļĩāđāđāļāļĩāđāļĒāļ§āļāđāļāļ
- āļĄāļĩāļāļĢāļ°āļŠāļāļāļēāļĢāļāđāļāđāļēāļ āļāļēāļĢāđāļāļīāļāļāļēāļĢāļāļāļēāļāļēāļĢāļŦāļĢāļ·āļāļāļēāļāļŠāļīāļāđāļāļ·āđāļāđāļĄāđāļāđāļģāļāļ§āđāļē 10 āļāļĩ
- āļĄāļĩāļāļ§āļēāļĄāļĨāļ°āđāļāļĩāļĒāļāļĢāļāļāļāļāļ āļĄāļĩāļāļ§āļēāļĄāļāļīāļāļŠāļĢāđāļēāļāļŠāļĢāļĢāļāđ
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- āļŦāļēāļāļĄāļĩāļāļĢāļ°āļŠāļāļāļēāļĢāļāđāļāļēāļĢāđāļāđ SAP āļāļ°āđāļāđāļĢāļąāļāļāļēāļĢāļāļīāļāļēāļĢāļāļēāđāļāđāļāļāļīāđāļĻāļĐ
- āļŠāļēāļĄāļēāļĢāļāļāļģāļāļēāļāļ āļēāļĒāđāļāđāļ āļēāļ§āļ°āļāļāļāļąāļāđāļāđ.
āļāļąāļāļĐāļ°:
Data Analysis, Finance, SQL, English
āļāļĢāļ°āđāļ āļāļāļēāļ:
āļāļēāļāļāļĢāļ°āļāļģ
āđāļāļīāļāđāļāļ·āļāļ:
āļŠāļēāļĄāļēāļĢāļāļāđāļāļĢāļāļāđāļāđ
- Data Analysis: Conduct in-depth analysis of retail and wholesale business data to address specific business questions and challenges.
- Insight Generation: Interpret results from dashboards and data analyses to develop actionable insights and strategic recommendations.
- Requirement Gathering: Identify business problems, gather requirements, and propose potential solutions, including leveraging AI to enhance business operations.
- ML Model creation: Create data analytic model including both deterministic and machine learning model.
- AI vendors coordination: Collaborate with external AI suppliers to align project objectives with technological capabilities.
- Cross-Departmental Collaboration: Work with various departments to develop and implement data-driven strategies that optimize business processes and decision-making.
- Communication: Act as a liaison between stakeholders and AI vendors, ensuring clear communication and understanding of project requirements.
- Data analytics and AI Strategy Design: Design and recommend how Business Intelligence (BI) and AI technologies can address business problems and provide further insights.
- Decision-making support: Present key findings from own analysis and strategic recommendations to business counterparts and senior management, focusing on project approaches and strategic planning.
- Master's degree in Finance, Business, Engineering, or a related field.
- Strong business acumen, with a deep understanding of retail and wholesale business.
- 3+ years of proven experience as a data analytic role (Retail or E-Commerce business is preferable).
- Hands-on Experience in SQL, data cloud platform (e.g., Databricks, Snowflake, GCP, or AWS), and high proficiency in Excel.
- Good Knowledge of Statistics.
- Experience in Python (Pandas, Numpy, SparkSQL), Data Visualisation (Tableau, PowerBI) is a plus.
- Excellent communication skills with the ability to convey complex findings to non-technical stakeholders.
- Fluent in Thai and English.
- Having a good attitude toward teamwork and willing to work hard.
- CP AXTRA | Lotus's
- CP AXTRA Public Company Limited.
- Nawamin Office: Buengkum, Bangkok 10230, Thailand.
- By applying for this position, you consent to the collection, use and disclosure of your personal data to us, our recruitment firms and all relevant third parties for the purpose of processing your application for this job position (or any other suitable positions within Lotus's and its subsidiaries, if any). You understand and acknowledge that your personal data will be processed in accordance with the law and our policy. .
āļāļąāļāļĐāļ°:
Power BI, Excel, Microsoft Office
āļāļĢāļ°āđāļ āļāļāļēāļ:
āļāļēāļāļāļĢāļ°āļāļģ
āđāļāļīāļāđāļāļ·āļāļ:
āļŠāļēāļĄāļēāļĢāļāļāđāļāļĢāļāļāđāļāđ
- āļ§āļīāđāļāļĢāļēāļ°āļŦāđ āđāļĨāļ°āļāļąāļāļāļģāļĢāļēāļĒāļāļēāļāđāļāļĢāļĩāļĒāļāđāļāļĩāļĒāļāļāđāļāļĄāļđāļĨ āļāđāļēāļāđ āđāļāđāļ āļĢāļēāļĒāļ§āļąāļ, āļĢāļēāļĒāđāļāļ·āļāļ, āļĢāļēāļĒāļāļĩ āđāļāļĒāđāļāļĢāļĩāļĒāļāđāļāļĩāļĒāļāļāļąāļāļāđāļāļĄāļđāļĨāđāļāļāļāļĩāļāđāļāđāļēāļŦāļĄāļēāļĒ āđāļĨāļ°āļāļēāļĢāļāļĢāļ°āļĄāļēāļāļāļēāļĢāđāļāļāļāļēāļāļ āđāļāļ·āđāļāļāļģāđāļŠāļāļāļāļđāđāļāļĢāļīāļŦāļēāļĢ.
- āļāļąāļāļāļģāļāļāļāļĢāļ°āļĄāļēāļāļāļĢāļ°āļāļģāļāļĩāđāļāļŠāđāļ§āļāļāļāļ āļĒāļāļāļāļēāļĒ āđāļĨāļ°āļāđāļēāđāļāđāļāđāļēāļĒāļŠāđāļāđāļŠāļĢāļīāļĄāļāļēāļĢāļāļēāļĒ.
- āļāļąāļāļāļģāļĢāļēāļĒāļāļēāļāļŠāļĢāļļāļ Profit & Loss āļāļĢāļ°āļāļģāđāļāļ·āļāļāđāļāļĄāļļāļĄāļĄāļāļāļāļĢāļīāļŦāļēāļĢāļŊ āđāļāļ·āđāļāļāļģāđāļŠāļāļāļāļđāđāļāļĢāļīāļŦāļēāļĢ.
- āļāļąāļāļāļģāļāļēāļāļāđāļāļĄāļđāļĨāļāļēāļĄāđāļāļ§āļāļēāļāļāđāļēāļāđ āđāļāļ·āđāļāļŠāļāļąāļāļŠāļāļļāļ āļāļēāļĢāļāļģāļāļ§āļ/āļāļēāļĢāļāļģāļĢāļēāļĒāļāļēāļāļāļāļāļŠāđāļ§āļāļāļēāļāļāļ·āđāļāđ āļāļĩāđāđāļāļĩāđāļĒāļ§āļāđāļāļ āđāļāđāļ āđāļāđāļēāļŦāļĄāļēāļĒāļāļēāļĢāļāđāļēāļĒāđāļāļīāļāļāļđāļāđāļāđāļŦāđāļāļąāļāļāļāļąāļāļāļēāļ, āđāļāđāļēāļŦāļĄāļēāļĒāļāļēāļĢāļāđāļēāļĒāđāļāļīāļāļĢāļēāļāļ§āļąāļĨ āđāļāđāļāļāđāļ.
- āļāļāļāđāļāļ Dashboard āļŠāļģāļŦāļĢāļąāļāļāđāļāļĄāļđāļĨāļāđāļēāļāļāđāļēāļāđ āļāļĩāđāđāļāļĩāđāļĒāļ§āļāđāļāļ āđāļŦāđāļāļđāđāđāļāđāļāļēāļāđāļāđāļēāđāļ āđāļĨāļ°āļŠāļēāļĄāļēāļĢāļāļāļģāđāļāđāļāđāđāļāđāļāđāļēāļĒ āđāļāđāļ Power BI.
- āļāļāļāđāļāļ Template āđāļŦāđāļŠāđāļ§āļāļāļēāļāļāđāļēāļāđāļāļĩāđāđāļāļĩāđāļĒāļ§āļāđāļāļ āđāļāļ·āđāļāļĢāļāļāļĢāļąāļāļāļēāļĢāļāļģāļāļēāļāđāļāļāļēāļĢāļĢāļ§āļāļĢāļ§āļĄāļāđāļāļĄāļđāļĨ āđāļāļ·āđāļāļāđāļ§āļĒāđāļŦāđāļāļēāļĢāļāļģāļāļēāļ āđāļāđāļāļĨāļĨāļąāļāļāđāļāļĩāđāļĢāļ§āļāđāļĢāđāļ§āļāļķāđāļ.
- āļ§āļīāđāļāļĢāļēāļ°āļŦāđāļāđāļāļĄāļđāļĨāļāļ·āđāļ āđ āļāļĩāđāđāļāļĩāđāļĒāļ§āļāđāļāļ āļāļēāļĄāļāļĩāđāđāļāđāļĢāļąāļāļĄāļāļāļŦāļĄāļēāļĒ.
- āđāļŦāđāļāļģāđāļāļ°āļāļģāđāļāļāļēāļ§āļīāđāļāļĢāļēāļ°āļŦāđāļāđāļāļĄāļđāļĨ āđāļāđāļŦāļāđāļ§āļĒāļāļēāļ āļāđāļēāļ āđ āļāļĩāđāļĄāļĩāļāļēāļĢāđāļāđāļāļēāļāļāđāļāļĄāļđāļĨ.
- āļāļēāļāļāļ·āđāļ āđ āļāļĩāđāđāļāđāļĢāļąāļāļĄāļāļāļŦāļĄāļēāļĒ.
- āļĄāļĩāļāļąāļāļĐāļ°āđāļāļāļēāļĢāđāļāđāļāļāļĄāļāļīāļ§āđāļāļāļĢāđāđāļāļĢāđāļāļĢāļĄ MS ExcelāļāļąāđāļāļŠāļđāļ.
- āļĄāļĩāļāļąāļāļĐāļ°āļāļēāļĢāđāļāđāļāļāļĄāļāļīāļ§āđāļāļāļĢāđāļāļ·āđāļāđ āđāļāđāđāļāđāļāļāļĒāđāļēāļāļāļĩ: Microsoft Office; Word, Power Point.
- āļĄāļĩāļāļ§āļēāļĄāļŠāļēāļĄāļēāļĢāļāļāļēāļāļāļēāļĢāļ§āļīāđāļāļĢāļēāļ°āļŦāđ āļāļēāļĢāļ§āļēāļāđāļāļ āđāļĨāļ°āļāļēāļĢāļāļąāļāļāļēāļĢāļāļĒāđāļēāļāđāļāđāļāļĢāļ°āļāļ āđāļĨāļ°āļĄāļĩāļĄāļēāļāļĢāļāļēāļāđāļāļāļēāļĢāļāļģāļāļēāļ.
- āļĄāļĩāļāļąāļāļĐāļ°āđāļāļāļēāļĢāļŠāļ·āđāļāļŠāļēāļĢ āđāļĨāļ°āļāļ§āļēāļĄāļŠāļēāļĄāļēāļĢāļ āđāļāļāļēāļĢāđāļāļĢāļāļē āļāđāļāļĢāļāļ.
- āļĄāļĩāļāļ§āļēāļĄāļŠāļēāļĄāļēāļĢāļāđāļāļāļēāļĢāđāļĢāļĩāļĒāļāļĢāļđāđāļŠāļīāđāļāđāļŦāļĄāđ āđ āđāļāđāļĢāļ§āļāđāļĢāđāļ§.
- āļĄāļĩāļāļąāļāļĐāļ°āđāļāļāļēāļĢāļāļģāđāļŠāļāļāļāļēāļ.
- āļĄāļĩāļāļąāļāļĐāļ°āđāļāļāļēāļĢāđāļāđ Power BI (āļāđāļēāļĄāļĩ).
- āļĄāļĩāļāļ§āļēāļĄāļĢāļđāđāļāļ·āđāļāļāļēāļāļāđāļēāļāļāļēāļĢāđāļāļĩāļĒāļāđāļāļĢāđāļāļĢāļĄāļāđāļēāļ āđ (āļāđāļēāļĄāļĩ).
- āļāļīāļāļāđāļāļŠāļāļāļāļēāļĄ.
- āļāļĢāļīāļĐāļąāļ āđāļĄāđāļāļīāļĢāđāļāđāļāļĢāļ āđāļĄāļāđāļāļāđāļĄāđāļāļāđ āļāļģāļāļąāļ.
- āļāļēāļāļēāļĢāđāļĨāđāļēāđāļāđāļāļāđāļ§āļ 1 āļāļąāđāļ 26 āļāļāļāļ§āļīāļ āļēāļ§āļāļĩāļĢāļąāļŠāļīāļ āđāļāļ§āļāļāļāļĄāļāļĨ āđāļāļāļāļāļļāļāļąāļāļĢ āđāļāļ§āļāļāļāļĄāļāļĨ āđāļāļāļāļāļļāļāļąāļāļĢ āļāļąāļāļŦāļ§āļąāļāļāļĢāļļāļāđāļāļāļĄāļŦāļēāļāļāļĢ.
āļāļĢāļ°āļŠāļāļāļēāļĢāļāđ:
1 āļāļĩāļāļķāđāļāđāļ
āļāļąāļāļĐāļ°:
Statistics, Data Analysis, Finance
āļāļĢāļ°āđāļ āļāļāļēāļ:
āļāļēāļāļāļĢāļ°āļāļģ
āđāļāļīāļāđāļāļ·āļāļ:
āļŠāļēāļĄāļēāļĢāļāļāđāļāļĢāļāļāđāļāđ
- Bachelor s degree in Statistics, Economics, Mathematics, or related field.
- 1-3 years of experience in data analysis or related roles. Experience in banking or finance preferred.
- Proficiency in Excel, SQL, Python/R, data visualization tools like Tableau or Power BI. Strong statistical analysis skills.
- Strong understanding of data analysis, statistical methods, and business insights. Knowledge of user personas and journey mapping in data-centric roles.
- Contact: [email protected] (K.Thipwimon).
- 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 .
āļāļąāļāļĐāļ°:
Data Analysis, Analytical Thinking, High Responsibilities
āļāļĢāļ°āđāļ āļāļāļēāļ:
āļāļēāļāļāļĢāļ°āļāļģ
āđāļāļīāļāđāļāļ·āļāļ:
āļŠāļēāļĄāļēāļĢāļāļāđāļāļĢāļāļāđāļāđ
- Analyze data and provide business insight.
- Assess risk and design mitigation actions based on data insight.
- Design dashboards / predictive models for decision-making management.
- Lead and Supervise team by projects.
āļāļąāļāļĐāļ°:
SQL, Research, Java
āļāļĢāļ°āđāļ āļāļāļēāļ:
āļāļēāļāļāļĢāļ°āļāļģ
āđāļāļīāļāđāļāļ·āļāļ:
āļŠāļēāļĄāļēāļĢāļāļāđāļāļĢāļāļāđāļāđ
- Background in SQL, databases and/or data science OR.
- BS/MS in software engineering, computer science, mathematics.
- Document data sources in enterprise data catalog with metadata, lineage and classification information.
- Develop aggregations and algorithms needed for reporting and analytics with low level complexity.
- Implement minor changes to existing data visualization applications, reporting dashboards.
- Document modifications to reporting applications based on modifications applied.
- Comprehend and adhere to all data security policies and procedures.
- Create data tools for analytics and data scientist team members.
- Build analytical tools to provide actionable insights into key business KPIs, etc.
- Work with data engineers to optimize pipelines for scalability and data delivery.
- Functional Competency.
- Working knowledge with data and analytics framework supporting data lakes, warehouses, marts, reporting, etc.
- Experience with data tools for visualizations, analytics and reporting.
- Strong analytical skills with ability to research, assess and develop observations/findings.
- Ability to communicate findings, approaches to cross functional teams and stakeholders.
- 3+ years' hands-on experience with a data science background.
- Some programming skills in Java, Python and SQL.
- Clear hands-on experience with database systems - Cloud technologies (e.g. AWS, Azure, Google), in-memory database systems (e.g. HANA, Hazel cast, etc) and other database systems - traditional RDBMS (e.g. Teradata, SQL Server, Oracle), and NoSQL databases (e.g. Cassandra, MongoDB, DynamoDB).
- Educational.
- Background in SQL, databases and/or data science OR.
- BS/MS in software engineering, computer science, mathematics.
- Document data sources in enterprise data catalog with metadata, lineage and classification information.
- Develop aggregations and algorithms needed for reporting and analytics with low level complexity.
- Implement minor changes to existing data visualization applications, reporting dashboards.
- Document modifications to reporting applications based on modifications applied.
- Comprehend and adhere to all data security policies and procedures.
- Create data tools for analytics and data scientist team members.
- Build analytical tools to provide actionable insights into key business KPIs, etc.
- Work with data engineers to optimize pipelines for scalability and data delivery.
- Functional Competency.
- Working knowledge with data and analytics framework supporting data lakes, warehouses, marts, reporting, etc.
- Experience with data tools for visualizations, analytics and reporting.
- Strong analytical skills with ability to research, assess and develop observations/findings.
- Ability to communicate findings, approaches to cross functional teams and stakeholders.
- 3+ years' hands-on experience with a data science background.
- Some programming skills in Java, Python and SQL.
- Clear hands-on experience with database systems - Cloud technologies (e.g. AWS, Azure, Google), in-memory database systems (e.g. HANA, Hazel cast, etc) and other database systems - traditional RDBMS (e.g. Teradata, SQL Server, Oracle), and NoSQL databases (e.g. Cassandra, MongoDB, DynamoDB).
āļāļąāļāļĐāļ°:
SQL, Data Warehousing, ETL, English
āļāļĢāļ°āđāļ āļāļāļēāļ:
āļāļēāļāļāļĢāļ°āļāļģ
āđāļāļīāļāđāļāļ·āļāļ:
āļŠāļēāļĄāļēāļĢāļāļāđāļāļĢāļāļāđāļāđ
- Work with business domain experts, data scientists and application developers to identify data that is relevant for analysis.
- Retrieve, prepare, and process a rich data variety of data sources.
- Apply data quality, cleaning and semantic inference techniques to maintain high data quality.
- Explore data sources to better understand the availability and quality/integrity of data.
- Gain fluency in AI/ML techniques.
- Experience with relational database systems with expertise in SQL.
- Experience in data management, data warehousing or unstructured data environments.
- Experience with data integration or ETL management tools such as Talend, Apache Airflow, AWS Glue, Google DataFlow or similar.
- Experience programming in Python, Java or other equivalent is a plus.
- Experience with Business Intelligence tools and platforms is a plus, e.g. Tableau, QlikView, Google DataStudio, Google Analytics or similar.
- Experience with Agile methodology and Extreme Programming is a plus.
- Ability to meet critical deadlines and prioritize multiple tasks in a fast-paced environment.
- Ability to work independently, have strong problem solving and organization skills, with a high initiative and a sense of accountability and ownership.
- Good communication in English.
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āļĒāļāļāļāļīāļĒāļĄ
āļĨāļāļāļāļģ 5 āļŠāļīāđāļāļāļĩāđāļŦāļĨāļąāļāđāļĨāļīāļāļāļēāļ āļāļĩāļ§āļīāļāļāļļāļāļāļ°āđāļāļĨāļĩāđāļĒāļāđāļāļāļĨāļāļāļāļēāļĨ
āļāļģāđāļāļ°āļāļģāļāđāļēāļāļāļēāļāļĩāļāļāļĢāļīāļĐāļąāļ 7 āđāļāļāļāļĩāđāļāļļāļāđāļĄāđāļāļ§āļĢāļāļģāļāļēāļāļāđāļ§āļĒ
āļāļģāđāļāļ°āļāļģāļāļēāļĢāļŦāļēāļāļēāļāđāļāļīāļāđāļāļĨāļŠāļļāļāļĒāļāļ 50 āļāļĢāļīāļĐāļąāļāļāļĩāđāļāļāļĢāļļāđāļāđāļŦāļĄāđāļāļĒāļēāļāļĢāđāļ§āļĄāļāļēāļāļāđāļ§āļĒāļĄāļēāļāļāļĩāđāļŠāļļāļ 2024
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