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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°āđāļ āļāļāļēāļ:
āļāļēāļāļāļĢāļ°āļāļģ
āđāļāļīāļāđāļāļ·āļāļ:
āļŠāļēāļĄāļēāļĢāļāļāđāļāļĢāļāļāđāļāđ
- TikTok is the leading destination for short-form mobile video. Our mission is to inspire creativity and bring joy. TikTok has global offices including Los Angeles, New York, London, Paris, Berlin, Dubai, Singapore, Jakarta, Seoul and Tokyo.
- Why Join Us.
- Creation is the core of TikTok's purpose. Our platform is built to help imaginations thrive. This is doubly true of the teams that make TikTok possible.
- Together, we inspire creativity and bring joy - a mission we all believe in and aim towards achieving every day.
- To us, every challenge, no matter how difficult, is an opportunity; to learn, to innovate, and to grow as one team. Status quo? Never. Courage? Always.
- At TikTok, we create together and grow together. That's how we drive impact - for ourselves, our company, and the communities we serve.
- Join us.
- TikTok is reshaping the current retail path to purchase and transforming how brands connect with their communities to drive shopping moments through an infinite loop of discovery, consideration, purchase, review and participation.
- To incubate and build the brand ecosystem on TikTok shop by managing and grow a portfolio of brands within TikTok shop by providing strategic roadmap and coordination together with cross functional teams.
- Support TH & VN routine data tracking, including JBP tracking, top brand policy tracking, weekly/monthly scorecard, brand diagnosis, data support, etc.
- Support TH & VN Mall banner operations on a routine basis.
- Other team support.
- 1-2 years of relevant experience, preferably within ecommerce.
- Proficient in Excel, data processing/tracking, data operations experience is preferred.
- Proficiency in English and local language.
- Good logic and analytical thinking.
- 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.
āļāļĢāļ°āđāļ āļāļāļēāļ:
āļāļēāļāļāļĢāļ°āļāļģ
āđāļāļīāļāđāļāļ·āļāļ:
āļŠāļēāļĄāļēāļĢāļāļāđāļāļĢāļāļāđāļāđ
- Design and implement the methods for storing and retrieving the data and monitoring the data pipelines, starting from the ingestion of raw data sources, transforming, cleaning and storing, enrich the data for promptly using for both of structured and unstructured data, and working with the data lake and cloud which is the big data technology.
- Develop data pipeline automation using Azure technologies, Databricks and Data Factory
- Understand data, reports and dashboards requirements, develop data visualization using Power BI, Tableau by working across workstreams to support data requirements including reports and dashboards and collaborate with data scientists, data analyst, data governance team or business stakeholders on several projects
- Analyze and perform data profiling to understand data patterns following Data Quality and Data Management processes
- 3 years+ experience in big data technology, data engineering, data analytic application system development.
- Have an experience of unstructured data for business Intelligence or computer science would be advantage.
- Technical skill in SQL, UNIX and Shell Script, Python, R Programming, Spark, Hadoop programming.
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.
āļāļąāļāļĐāļ°:
Employer Branding, Branding, Automation
āļāļĢāļ°āđāļ āļāļāļēāļ:
āļāļēāļāļāļĢāļ°āļāļģ
āđāļāļīāļāđāļāļ·āļāļ:
āļŠāļēāļĄāļēāļĢāļāļāđāļāļĢāļāļāđāļāđ
- Collect, clean, and integrate HR data from various sources, including HRMS, Excel, Google Sheets, and text files, to ensure accurate and reliable data for analysis.
- Develop and maintain HR analytical reports and dashboards, including executive dashboards, that support decision-making across HR areas, such as turnover analysis, Manpower, employer branding activities, etc.
- Implement HR automation processes to streamline data workflows.
- Coordinate and execute project-related data analytics tasks and ad-hoc projects, ensuring timely and accurate completion.
- Stay updated on emerging technologies, especially AI, to enhance HR data solutions.
- Education: Bachelor s Degree in IT, Computer Science, Computer Engineering, Data Analytics, or a related field. An advanced degree in a relevant area is a plus.
- Dashboard Tools: Proficiency in creating and managing interactive dashboards using Power BI Desktop, Power BI Service, Looker Studio, and Excel.
- Data Analysis Tools: Strong experience in data analysis and transformation using Power Query, SQL, and Python, including data extraction, transformation, and query optimization.
- Application Tools: Knowledge of and experience with no-code/low-code platforms (e.g., Power Apps, AppSheet) for creating applications and automating workflows.
- Project Management: Proven ability to manage multiple projects simultaneously, adapt to fast-paced environments, and consistently meet deadlines.
- Communication Skills: Strong verbal and written communication skills, with the ability to present complex, data-driven insights in a clear and impactful manner to non-technical stakeholders.
āļāļĢāļ°āđāļ āļāļāļēāļ:
āļāļēāļāļāļĢāļ°āļāļģ
āđāļāļīāļāđāļāļ·āļāļ:
āļŠāļēāļĄāļēāļĢāļāļāđāļāļĢāļāļāđāļāđ
- āļ§āļīāđāļāļĢāļēāļ°āļŦāđ āļāļīāļāļēāļĢāļāļē āđāļĨāļ°āļāļāļāļ§āļāļ§āļāđāļāļīāļāļŠāļīāļāđāļāļ·āđāļāđāļāđāļĨāļđāļāļāđāļēāđāļāļāļēāļĢāļāļ·āđāļāļāļēāļĒ āđāļāļ·āđāļāļāđāļāļāļēāļĢāļāļģāļĢāļ°āđāļāļīāļ āđāļĨāļ°āļŦāļĨāļąāļāļāļĢāļ°āļāļąāļ āļāļĩāđāđāļŦāļĄāļēāļ°āļŠāļĄ āļ āļēāļĒāđāļāđāļāļ§āļēāļĄāđāļŠāļĩāđāļĒāļāļāļĩāđāļĒāļāļĄāļĢāļąāļāđāļāđ āļāļĨāļāļāļāļāļāļĢāļ§āļāļŠāļāļāđāļĨāļ°āļāļĢāļąāļāļāļĢāļļāļāļāđāļāļĄāļđāļĨāļĨāļđāļāļāđāļēāđāļāļĢāļ°āļāļāđāļŦāđāļāļđāļāļāđāļāļ āļāļĢāļāļāđāļ§āļ āđāļĨāļ°āđāļāđāļāļāļąāļāļāļļāļāļąāļ
- āđāļŦāđāļāļ§āļēāļĄāđāļŦāđāļāļāđāļēāļāđāļāļĢāļāļīāļāļāļĢāļāļĩāļāļēāļĢāļāļēāļĒāļāđāļēāļāļāļģāļŠāļąāđāļāļāļ·āđāļāļāļĩāđāļĄāļĩāļŠāļāļēāļāļ°āđāļāļīāļāļ§āļāđāļāļīāļāđāļĨāļ°/āļŦāļĢāļ·āļāđāļāļīāļāļāļģāļŦāļāļāļāļģāļĢāļ°
- āļāļąāļāļāļē āļāļĢāļąāļāļāļĢāļļāļ āļĢāļ°āļāļāļāļēāļĢāļāļĢāļ°āđāļĄāļīāļāđāļāļĢāļāļāļēāļĢāļāđāļēāđāļĨāļ°āļĢāļ°āļāļāļāļēāļāļāđāļēāļāļŠāļīāļāđāļāļ·āđāļ āđāļāļ·āđāļāļĄāļĩāļĢāļ°āļāļāļāļēāļāļāđāļāļĄāļđāļĨāļāļĩāđāļāļĢāļāļāđāļ§āļ āļĢāļ§āļāđāļĢāđāļ§ āđāļĨāļ°āļĄāļĩāļāļĢāļ°āļŠāļīāļāļāļīāļ āļēāļāđāļāļīāđāļĄāļāļķāđāļ
- 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
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- āļĄāļĩāļāļąāļāļĐāļ°āđāļāļāļēāļĢāļ§āļēāļāđāļāļ āļāļĢāļīāļŦāļēāļĢāļāļąāļāļāļēāļĢ āđāļāļĢāļāļēāļāđāļāļĢāļāļ āđāļĨāļ°āđāļāđāļāļąāļāļŦāļēāđāļāļāļēāļ°āļŦāļāđāļēāđāļāđāļāļĩ
- āļĄāļĩāļāļąāļāļĐāļ°āđāļāļāļēāļĢāļ§āļīāđāļāļĢāļēāļ°āļŦāđāđāļĨāļ°āđāļŦāđāļāļ§āļēāļĄāđāļŦāđāļāļāļēāļĄāļāļĩāđāļāļāđāļāļāļāđ āļāđāļĒāļāļēāļĒ āđāļĨāļ°āļĢāļ°āđāļāļĩāļĒāļ
- āļĄāļāļļāļĐāļĒāļŠāļąāļĄāļāļąāļāļāđāļāļĩāđāļĨāļ°āļĄāļĩāļāļąāļāļĐāļ°āļāļēāļĢāļŠāļ·āđāļāļŠāļēāļĢ āļāļīāļāļāđāļāļāļĢāļ°āļŠāļēāļāļāļēāļāđāļāđāđāļāđāļāļāļĒāđāļēāļāļāļĩ
- 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°:
SAP
āļāļĢāļ°āđāļ āļāļāļēāļ:
āļāļēāļāļāļĢāļ°āļāļģ
āđāļāļīāļāđāļāļ·āļāļ:
āļŠāļēāļĄāļēāļĢāļāļāđāļāļĢāļāļāđāļāđ
- Design, build and configure applications to meet business process and application requirements.
- Keep abreast of current and emerging SAP technologies and trends and assist in the selection of robust and appropriate technologies.
- Design and deliver retail-specific processes and end to end functional configuration in SAP.
- Demonstrate technical expertise in end-to-end configuration of sales and supply chain processes in SAP (or S/4 HANA).
- At least 1 year of experience as SAP SD Consultant.
- Degree in Information Technology, Computer Science or another relevant field.
- Hands on experience in Implementation or Data Migration is a must.
- Experience in data migration is a must.
āļāļąāļāļĐāļ°:
Compliance, Risk Management, SAP
āļāļĢāļ°āđāļ āļāļāļēāļ:
āļāļēāļāļāļĢāļ°āļāļģ
āđāļāļīāļāđāļāļ·āļāļ:
āļŠāļēāļĄāļēāļĢāļāļāđāļāļĢāļāļāđāļāđ
- Develop Data Governance Framework and data management strategy & roadmap.
- Develop data policy, rules and regulations to control data management standard control ensure compliance with data management, data privacy and global regulations.
- Take the lead in managing data governance framework, data policy and standards with a focus on establishing and ensuring organization adherence to an enterprise data governance framework.
- Partner with Risk Management, Security and Compliance teams to identify related regulatory requirements and implications such as PDPA/GDPR and drive gap assessment and improvement to data policy, standards and governance processes.
- Develop and own the data sharing principles, operational model and process to govern data exchanges with external data ecosystem.
- Review and advise on data sharing to 3rd parties to approval protocol, data usage conditions, as well as data minimization practice.
- Communicate and engage various projects to ensure activities of data management in assigned projects to comply with data management standard and controls.
- Provide stakeholders a thorough advice on data accessibility, and/or data management based on deep understanding of data use cases, data Lake and its ecosystem architecture.
- Drive Data Stewardship and Data Governance program and enterprise-wide data governance awareness throughout the organization including identifying a phased approach to progress and mature the program.
- EDUCATION.
- Bachelor s degree in Computer Science, Computer Engineering, Information Technology, or related discipline.
- EXPERIENCE.
- Experience in writing or working with various documents such as policy, standard, procedure, and guideline.
- Knowledge of data platform concept, data lake.
- Advanced knowledge of data management life cycle.
- Good communication, strong analytical and problem-solving skills.
- Experience in data governance or data management.
- Experience in design of data platform, data engineer.
- Experience business-driven data governance practices implementation.
- Experience with on premised and cloud service eg AWS, Azure, SAP.
- Experience with data pipeline and data management tools.
- Experience in writing or working with various documents such as policy, standard, procedure, and guideline.
- OTHER REQUIREMENTS.
āļāļąāļāļĐāļ°:
Data Analysis, ETL, Data Warehousing
āļāļĢāļ°āđāļ āļāļāļēāļ:
āļāļēāļāļāļĢāļ°āļāļģ
āđāļāļīāļāđāļāļ·āļāļ:
āļŠāļēāļĄāļēāļĢāļāļāđāļāļĢāļāļāđāļāđ
- Data Architecture: Design, develop, and maintain the overall data architecture and data pipeline systems to ensure efficient data flow and accessibility for analytical purposes.
- Data Integration: Integrate data from multiple sources, including point-of-sale systems, customer databases, e-commerce platforms, supply chain systems, and other relevant data sources, ensuring data quality and consistency.
- Data Modeling: Design and implement data models that are optimized for scalability, ...
- Data Transformation and ETL: Develop and maintain efficient Extract, Transform, and Load (ETL) processes to transform raw data into a structured format suitable for analysis and reporting.
- Data Warehousing: Build and maintain data warehouses or data marts that enable efficient storage and retrieval of structured and unstructured data for reporting and analytics purposes.
- Data Governance and Security: Establish and enforce data governance policies and procedures, including data privacy and security measures, to ensure compliance with industry regulations and protect sensitive data.
- Data Quality and Monitoring: Implement data quality checks and monitoring mechanisms to identify and resolve data inconsistencies, anomalies, and issues in a timely manner.
- Collaboration: Collaborate with cross-functional teams, including data scientists, business analysts, and software engineers, to understand their data needs, provide data solutions, and support their analytical initiatives.
- Performance Optimization: Optimize data processing and query performance to ensure efficient data retrieval and analysis, considering factors such as data volume, velocity, and variety.
- Documentation: Maintain documentation of data processes, data flows, data models, and system configurations, ensuring accuracy and accessibility for future reference.
- Bachelor's or master's degree in computer science, information systems, or a related field.
- Strong programming skills in languages such as Python, SQL. C++ is plus.
- At least 5 year experience with data modeling, database design, and data warehousing concepts.
- Proficiency in working with relational databases (e.g., MySQL, PostgreSQL) and big data technologies (e.g., Hadoop, Spark, Hive).
- Familiarity with cloud-based data platforms, such as AWS.
- Knowledge of ETL tools and techniques for data integration and transformation.
- Understanding of data governance, data security, and regulatory compliance requirements.
- Excellent problem-solving skills and attention to detail.
- Strong communication and interpersonal skills to collaborate effectively with cross-functional teams.
- Ability to work in a fast-paced environment and handle multiple projects simultaneously.
- Location: BTS Ekkamai
- Working Day: Mon-Fri.
āļāļąāļāļĐāļ°:
Assurance
āļāļĢāļ°āđāļ āļāļāļēāļ:
āļāļēāļāļāļĢāļ°āļāļģ
āđāļāļīāļāđāļāļ·āļāļ:
āļŠāļēāļĄāļēāļĢāļāļāđāļāļĢāļāļāđāļāđ
- Develop and analyze Enterprise Service revenue to understand Product and Service trends within the AIS Group, ensuring that revenue collection, promotion packages, and new services are properly executed according to the company s business conditions.
- Identify suitable QA methods to reduce revenue loss and prevent errors in the Line Operation's work, sharing knowledge to strengthen revenue assurance.
- Verify the completeness and accuracy of service calculations, promotion packages, and offerings for corporate customers.
- Review the calculation of Postpaid Voice and IDD services, as well as IR services in the RBM system, ensuring they are correct, complete, and in line with the rates and conditions set by the company, with no abnormalities that could lead to revenue loss (Real Loss/Opportunity Loss).
- Communicate, coordinate, and follow up on issues causing revenue loss, identify the root causes, and work with relevant departments to resolve the problems, reducing revenue loss, particularly in Voice services.
- Analyze data from various sources within the scope of responsibility using data analytics skills, reflecting trends, performance, efficiency, and effectiveness of Products and Services. Identify abnormalities impacting revenue loss (Real Loss/Opportunity Loss & Fraud), and conduct audits and monitoring.
- Prepare analysis reports to support management strategies and assess risks in various areas.
- Perform other duties as assigned by the supervisor.
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.
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].
āļāļąāļāļĐāļ°:
Positive Thinker, English
āļāļĢāļ°āđāļ āļāļāļēāļ:
āļāļēāļāļāļĢāļ°āļāļģ
āđāļāļīāļāđāļāļ·āļāļ:
āļŠāļēāļĄāļēāļĢāļāļāđāļāļĢāļāļāđāļāđ
- Be the contact for commercial queries, leading the resolution in a client-focused and timely manner in cooperation with Client Liaison and Commercial teams. To be able to identify, investigate and coordinate the resolution of data, process, or product related queries.
- Plan and execute a complex daily personal workload and support to meet departmental and company schedules.
- Analyze and identify gaps and areas for improvement in coding, data input validation ...
- Providing accurate and timely feedback to respective country teams, driving SOP and use of best-demonstrated practices.
- Working across relevant Operations/Enablement teams to ensure delivery to client expectations and satisfaction.
- Operate in a virtual/multi-cultural environment, liaising with stakeholders and colleagues.
- QualificationsBachelor s degree in any field.
- A high degree of accuracy, proactivity, and attention to detail.
- Good analytics skills and aptitude for data and operational processes.
- Good in written and verbal communication skills in English.
- A positive thinker and a good Team Player.
- Additional Information
- Our Benefits.
- Flexible working environment.
- Volunteer time off.
- LinkedIn Learning.
- Employee-Assistance-Program (EAP).
- About NIQ.
- NIQ is the world s leading consumer intelligence company, delivering the most complete understanding of consumer buying behavior and revealing new pathways to growth. In 2023, NIQ combined with GfK, bringing together the two industry leaders with unparalleled global reach. With a holistic retail read and the most comprehensive consumer insights delivered with advanced analytics through state-of-the-art platforms NIQ delivers the Full View&trade. NIQ is an Advent International portfolio company with operations in 100+ markets, covering more than 90% of the world s population.
- For more information, visit NIQ.com.
- Want to keep up with our latest updates?.
- Follow us on: LinkedIn | Instagram | Twitter | Facebook.
- Our commitment to Diversity, Equity, and Inclusion.
- NIQ is committed to reflecting the diversity of the clients, communities, and markets we measure within our own workforce. We exist to count everyone and are on a mission to systematically embed inclusion and diversity into all aspects of our workforce, measurement, and products. We enthusiastically invite candidates who share that mission to join us. We are proud to be an Equal Opportunity/Affirmative Action-Employer, making decisions without regard to race, color, religion, gender, gender identity or expression, sexual orientation, national origin, genetics, disability status, age, marital status, protected veteran status or any other protected class. Our global non-discrimination policy covers these protected classes in every market in which we do business worldwide. Learn more about how we are driving diversity and inclusion in everything we do by visiting the NIQ News Center: https://nielseniq.com/global/en/news-center/diversity-inclusion.
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°:
Big Data, ETL, SQL
āļāļĢāļ°āđāļ āļāļāļēāļ:
āļāļēāļāļāļĢāļ°āļāļģ
āđāļāļīāļāđāļāļ·āļāļ:
āļŠāļēāļĄāļēāļĢāļāļāđāļāļĢāļāļāđāļāđ
- Develop and maintain robust data pipelines to ingest, process, and transform raw data into formats suitable for LLM training.
- Conduct meeting with users to understand the data requirements and perform database design based on data understanding and requirements with consideration for performance.
- Maintain data dictionary, relationship and its interpretation.
- Analyze problem and find resolution, as well as work closely with administrators to monitor performance and advise any necessary infrastructure changes.
- Work with business domain experts, data scientists and application developers to identify data that is relevant for analysis.
- Develop big data solutions for batch processing and near real-time streaming.
- Own end-to-end data ETL/ELT process framework from Data Source to Data warehouse.
- Select and integrate appropriate tools and frameworks required to provide requested capabilities.
- Design and develop BI solutions.
- Hands-on development mentality, with a willingness to troubleshoot and solve complex problems.
- Keep abreast of new developments in the big data ecosystem and learn new technologies.
- Ability to effectively work independently and handle multiple priorities.
- Bachelor degree or higher in Computer Science, Computer Engineering, Information Technology, Management Information System or an IT related field.
- 3+ year's experiences in Data Management or Data Engineer (Retail or E-Commerce business is preferrable).
- Expert experience in query language (SQL), Databrick SQL, PostgreSQL.
- Experience in Big Data Technologies like Hadoop, Apache Spark, Databrick.
- Experience in Python is a must.
- Experience in Generative AI is a must.
- Knowledge in machine/statistical learning, data mining is a plus.
- Strong analytical, problem solving, communication and interpersonal skills.
- Having good attitude toward team working 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. .
āļāļĢāļ°āļŠāļāļāļēāļĢāļāđ:
3 āļāļĩāļāļķāđāļāđāļ
āļāļąāļāļĐāļ°:
ETL, DevOps, Automation, Big Data, SQL
āļāļĢāļ°āđāļ āļāļāļēāļ:
āļāļēāļāļāļĢāļ°āļāļģ
āđāļāļīāļāđāļāļ·āļāļ:
āļŠāļēāļĄāļēāļĢāļāļāđāļāļĢāļāļāđāļāđ
- Data Pipeline Development: Design, implement, and optimize scalable ETL/ELT pipelines to ingest, transform, and store structured and unstructured data in a cloud environment (AWS is a core but not limit).
- Machine Learning Pipeline Development: Work collaboratively with data scientists to productionize and maintain scalable machine learning services. The solutions encompass a variety of approaches, including traditional and near real-time machine learning, deployed across multi-state service architectures.
- Data Platform: Collaborate closely with DevOps and infrastructure teams to design, implement, and manage scalable data storage and processing platforms. Leverage AWS services such as S3, Redshift, Glue, Lambda, Athena, and EMR to ensure performance, reliability, and cost-efficiency.
- Data Modeling and Schema Management: Develop and maintain robust data models and schemas to support analytics, reporting, and operational requirements. Adhere to the design principle of establishing a "single version of truth" to ensure consistency, accuracy, and reliability across all data-driven processes.
- Data/AI Quality-as-a-Service Development: Design, develop, and maintain scalable "Data/AI Quality-as-a-Service" solutions, adhering to zero-ops design principles. The scope of quality includes monitoring data drift, analyzing performance metrics, and detecting model drift to ensure consistent, reliable, and high-performing AI systems.
- Cross-Functional Collaboration: Collaborate closely with data scientists, analysts, and application developers to ensure the seamless integration of data solutions into workflows, enhancing functionality and enabling data-driven decision-making.
- Automation & Monitoring: Design and implement robust monitoring and automation frameworks to ensure the high availability, performance, and cost-efficiency of data workflows, guided by the principle of "Zero Ops by Design.".
- Compliance & Security: Uphold data security, privacy, and compliance with banking regulations and industry standards, ensuring all solutions meet rigorous governance requirements.
- Continuous Improvement: Stay informed about emerging technologies and trends in cloud data engineering, advocating for their adoption to enhance system capabilities and maintain a competitive edge.
- Educational BackgroundBachelor's degree in Computer Science, Computer Engineering, Data Engineering, or a related field.
- Experience3+ years of experience in cloud data engineering or similar roles.
- Proven expertise in cloud data technologies.
- Hands-on experience with big data technologies such as Apache Spark.
- Technical SkillsProficiency in SQL and programming languages such as Python, Java, or Scala.
- Expertise in data pipeline and workflow orchestration tools for both batch and real-time processing (e.g., Apache Airflow, AWS Step Functions).
- Understanding of data warehouse and lakehouse architectures.
- Familiarity with software development best practices, including SDLC concepts, CI/CD/(+CL) pipelines, and Infrastructure as Code tools (e.g., Terraform, AWS CloudFormation).
- Other SkillsStrong problem-solving and analytical thinking capabilities.
- Excellent communication and collaboration skills.
- Preferred QualificationsAWS Data Analytics - Specialty certification or equivalent experience.
- Experience in banking or fintech environments. Understanding of financial data and regulatory requirements.
- Familiarity with real-time data processing and stream analytics.
- Experience in working end-to-end with data scientists and analysts as part of "AnalyticsOps" to develop and maintain ML/AI services is a strong advantage.
- 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°:
Cloud Computing, SAP, Linux
āļāļĢāļ°āđāļ āļāļāļēāļ:
āļāļēāļāļāļĢāļ°āļāļģ
āđāļāļīāļāđāļāļ·āļāļ:
āļŠāļēāļĄāļēāļĢāļāļāđāļāļĢāļāļāđāļāđ
- Acting as the key of Cloud technical aspect for the consulting team to provide the technical consulting to both internal and external customers.
- Design Cloud solution architecture in response to the client s requirement.
- Provide advisory consulting service to the client regarding the True IDC Consulting practices.
- Create Cloud technical requirements to the client s migration plan.
- Experience of designing and implementing comprehensive Cloud computing solutions on various Cloud technologies e.g. AWS, GCP.
- Experience in building multi-tier Service Oriented Architecture (SOA) applications.
- Experience in SAP Cloud Infrastructure in term of architecture & design in AWS, GCP public cloud.
- Knowledge of Linux, Windows, Apache, IIS, NoSQL operations as its architecture toth e Cloud.
- Knowledge of Containerization administrative for both Windows and Linux technologies.
- Knowledge of key concerns and how they are addressed in Cloud Computing such as security, performance and scalability.
- Good in customer objective handling & Good in customer presentation skill.
- Nice to have.
- UNIX shell scripting.
- AWS Certified Solution Architect - Associate.
- GCP Certified Solution Architect - Associate.
āļāļąāļāļĐāļ°:
Statistics, Data Analysis, SQL
āļāļĢāļ°āđāļ āļāļāļēāļ:
āļāļēāļāļāļĢāļ°āļāļģ
āđāļāļīāļāđāļāļ·āļāļ:
āļŠāļēāļĄāļēāļĢāļāļāđāļāļĢāļāļāđāļāđ
- Data Science Foundations: Strong foundation in data science, statistics, and advanced data analytics, including data visualization to communicate insights effectively.
- Exploratory Data Analysis (EDA): Skilled in performing EDA to uncover patterns, detect anomalies, and generate meaningful insights from data.
- Experimentation & Testing: Skilled in designing A/B tests or other experimental designs to measure business impact, analyze results, and communicate findings clearly to stakeholders.
- Machine Learning & AI.
- Model Development & Deployment: Experience in building, deploying, and optimizing machine learning models on large datasets.
- Generative AI (GenAI): Opportunity to work on GenAI projects that drive innovation and impactful business solutions.
- Problem-Solving & Collaboration.
- Analytical & Problem-Solving Skills: Strong analytical and problem-solving abilities focused on deriving actionable insights from data.
- Team Collaboration: Ability to work effectively both independently and as part of a collaborative team, contributing to shared project goals.
- Technical Expertise.
- Proficiency in Big Data Technologies: Expertise in Spark, PySpark, and SQL for large-scale data processing focused on feature creation for machine learning models and data analysis tasks.
- Programming Skills: Strong proficiency in Python for data analysis and machine learning (including libraries like Pandas, PySpark, Scikit-learn, XGBoost, LightGBM, Matplotlib, Plotly, Seaborn, etc.).
- Python Notebooks: Familiarity with Jupyter, Google Colab, or Apache Zeppelin for interactive data analysis and model development.
- Platform Experience: Experience in using PySpark on cloud platforms such as Azure Databricks or other platforms (including on-premise) is a plus.
- Education & Experience.
- Educational Background: Bachelor s or advanced degree in Data Science, Statistics, Computer Science, Computer Engineering, Mathematics, Information Technology, Engineering, or related fields.
- Work Experience: At least 2-3 years of relevant experience in Data Science, Analytics, or Machine Learning, with demonstrated technical expertise and a proven track record of driving data-driven business solutions.
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āļĒāļāļāļāļīāļĒāļĄ
āļĨāļāļāļāļģ 5 āļŠāļīāđāļāļāļĩāđāļŦāļĨāļąāļāđāļĨāļīāļāļāļēāļ āļāļĩāļ§āļīāļāļāļļāļāļāļ°āđāļāļĨāļĩāđāļĒāļāđāļāļāļĨāļāļāļāļēāļĨ
āļāļģāđāļāļ°āļāļģāļāđāļēāļāļāļēāļāļĩāļāļāļĢāļīāļĐāļąāļ 7 āđāļāļāļāļĩāđāļāļļāļāđāļĄāđāļāļ§āļĢāļāļģāļāļēāļāļāđāļ§āļĒ
āļāļģāđāļāļ°āļāļģāļāļēāļĢāļŦāļēāļāļēāļāđāļāļīāļāđāļāļĨāļŠāļļāļāļĒāļāļ 50 āļāļĢāļīāļĐāļąāļāļāļĩāđāļāļāļĢāļļāđāļāđāļŦāļĄāđāļāļĒāļēāļāļĢāđāļ§āļĄāļāļēāļāļāđāļ§āļĒāļĄāļēāļāļāļĩāđāļŠāļļāļ 2025
āļāđāļēāļ§āļŠāļēāļĢāđāļŦāļĄāđāđ