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āļāļĢāļ°āļŠāļāļāļēāļĢāļāđ:
āđāļĄāđāļāļģāđāļāđāļāļāđāļāļāļĄāļĩāļāļĢāļ°āļŠāļāļāļēāļĢāļāđāļāļģāļāļēāļ
āļāļąāļāļĐāļ°:
Python, SQL, Database Administration, English, Thai
āļāļĢāļ°āđāļ āļāļāļēāļ:
āļāļēāļāļāļĢāļ°āļāļģ
āđāļāļīāļāđāļāļ·āļāļ:
āļŋ35,000 - āļŋ45,000, āļŠāļēāļĄāļēāļĢāļāļāđāļāļĢāļāļāđāļāđ
- Guide and train new customers to confidently use our system.
- Monitor customer activity, troubleshoot basic issues, and coordinate with internal teams.
- Analyze and manage customer data to ensure readiness for real-time use.
- Work closely with logistics, operations, and tech teams to deliver a seamless onboarding experience.
- Travel and visit customer sites.
- Experience in Customer Support or Data Analysis is a plus new graduates are welcome to apply.
- Proficiency in Excel and SQL; Python skills are a plus.
- Excellent communication skills in both Thai and English.
- Adaptable, quick to learn, and able to work under pressure.
- Educational background in IT, Computer Science, or related fields is preferred.
- Allows you to apply your skills in data, technology, and customer service.
- Supports your personal and professional development.
āļāļĢāļ°āđāļ āļāļāļēāļ:
āļāļēāļāļāļĢāļ°āļāļģ
āđāļāļīāļāđāļāļ·āļāļ:
āļŠāļēāļĄāļēāļĢāļāļāđāļāļĢāļāļāđāļāđ
- Education: Information Systems, Data Analytics, Marketing Technology, or Business Intelligence.
- Experience: 3-5 years in system management, data analysis, or MarTech projects.
- Customer segmentation and data analysis.
- Process optimization and system integration skills.
- Collaboration with IT and data teams for reporting enhancement.
- Understanding of customer journey analytics.
- āļāđāļēāļāđāļāđāļāđāļēāļāđāļĨāļ°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°āļŠāļāļāđāđāļāļĢāļāļīāļāļēāļĢāļāļēāļĢāļąāļāļāļļāļāļāļĨāđāļāđāļēāļāļģāļāļēāļ 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°:
Statistics, Python, SQL
āļāļĢāļ°āđāļ āļāļāļēāļ:
āļāļēāļāļāļĢāļ°āļāļģ
āđāļāļīāļāđāļāļ·āļāļ:
āļŠāļēāļĄāļēāļĢāļāļāđāļāļĢāļāļāđāļāđ
- Lead the end-to-end development of analytics and AI projects that strengthen business and customer intelligence across the Siam Piwat data ecosystem.
- Translate business challenges into data science solutions that deliver measurable outcomes and strategic value.
- Collaborate with MIS, Data Engineering, Data Analytics and Business teams to ensure reliable data flow, technical integration, and sustainable implementation.
- Drive analytics initiatives that inform sales strategies, tenant insights, and evolving market opportunities.
- Technical Execution & Supervision.
- Develop and deploy predictive models and analytics frameworks to understand performance patterns, identify growth drivers, and support decision-making.
- Provide technical direction and support to data scientists and analysts to maintain consistency, accuracy, and quality of analytical outputs.
- Apply best practices in data preparation, model governance, and MLOps to ensure scalability and reliability.
- Work closely with cross-functional partners internally and externally to transform analytical insights into actionable business recommendations.
- Business Impact & Innovation.
- Deliver insights that enhance understanding of customer behavior, tenant performance, and overall sales and customer trends.
- Monitor and assess emerging market and consumer trends to guide future business strategies.
- Present complex findings through clear visualizations and storytelling tailored for executive and business audiences.
- Foster a culture of innovation and continuous improvement in analytics practice.
- Education & Experience.
- Master s or Ph.D. in Data Science, Computer Science, Statistics, Mathematics, or a related quantitative discipline.
- Minimum 5-7 years of experience in data science, advanced analytics, or AI solution delivery..
- Demonstrated success in developing and implementing data-driven solutions with measurable business impact.
- Experience in retail, customer analytics, or digital transformation environments is preferred..
- Technical Skills.
- Proficiency in Python, SQL, and PySpark for data analysis and model development..
- Solid understanding of machine learning, AI, and statistical modeling frameworks..
- Familiarity with cloud data platforms (DWS, AWS, GCP, or Azure) and version control tools (Git)..
- Experience with visualization and BI tools such as Power BI, Tableau, Apache Superset, or similar..
āļāļąāļāļĐāļ°:
Power BI, Tableau, Statistics
āļāļĢāļ°āđāļ āļāļāļēāļ:
āļāļēāļāļāļĢāļ°āļāļģ
āđāļāļīāļāđāļāļ·āļāļ:
āļŠāļēāļĄāļēāļĢāļāļāđāļāļĢāļāļāđāļāđ
- Conduct data collection, cleaning, and exploratory analysis to support retail-related use cases such as customer insights and product performance.
- Assist in developing and evaluating machine learning models for forecasting, segmentation, recommendation, and customer behavior analysis.
- Support GenAI and LLM-related tasks, including text classification, summarization, embedding generation, prompt testing, and preparation of datasets for NLP or RAG-style workflows.
- Prepare and document features for analytics and machine learning workflows, ensuring data quality and reproducibility.
- Build dashboards and visualizations using Power BI, Tableau, Plotly, or matplotlib to present insights to business stakeholders.
- Collaborate with senior data scientists, data engineers, and business teams to understand requirements and translate them into analytical tasks.
- Participate in team knowledge-sharing sessions and continuously develop technical skills.
- Bachelor s degree in Data Science, Computer Science, Statistics, Mathematics, Engineering, or a related field.
- Minimum of two years of experience in data analytics or data science.
- Proficiency in Python, including pandas, numpy, and scikit-learn.
- Strong SQL skills for working with large datasets.
- Understanding of common machine learning techniques such as regression, classification, and clustering.
- Exposure to GenAI or LLM tools and libraries such as Hugging Face, LangChain, or OpenAI APIs.
- Experience with data visualization tools such as Tableau, Power BI, or matplotlib.
- Experience with basic NLP tasks such as tokenization, text cleaning, or embedding generation.
- Hands-on experimentation with LLMs or GenAI workflows.
- Familiarity with Git or collaborative coding practices.
- Experience in retail, e-commerce, or consumer analytics environments.
- 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:
7 āļāļĩāļāļķāđāļāđāļ
āļāļąāļāļĐāļ°:
Financial Modeling, Cash Flow Management, English
āļāļĢāļ°āđāļ āļāļāļēāļ:
āļāļēāļāļāļĢāļ°āļāļģ
āđāļāļīāļāđāļāļ·āļāļ:
āļŋ45,000 - āļŋ85,000, āļŠāļēāļĄāļēāļĢāļāļāđāļāļĢāļāļāđāļāđ
- āļāļĢāļīāļŦāļēāļĢāđāļĨāļ°āļāļģāļāļąāļāļāļđāđāļĨāļāļēāļĢāļ§āļīāđāļāļĢāļēāļ°āļŦāđāļāđāļāļĄāļđāļĨāļāļēāļāļāļēāļĢāđāļāļīāļāđāļĨāļ°āļāļēāļĢāļĨāļāļāļļāļ āđāļāļ·āđāļāļŠāļāļąāļāļŠāļāļļāļāļāļēāļĢāļāļąāļāļŠāļīāļāđāļāđāļāļīāļāļāļĨāļĒāļļāļāļāđ.
- āļāļąāļāļāļģ āļ§āļīāđāļāļĢāļēāļ°āļŦāđ āđāļĨāļ°āļāļģāđāļŠāļāļāļĢāļēāļĒāļāļēāļāļāļēāļāļāļēāļĢāđāļāļīāļ āļāļĨāļāļāļāđāļāļ āđāļĨāļ°āļāļĢāļ°āļĄāļēāļāļāļēāļĢāđāļāļĄāļīāļāļīāļāđāļēāļ āđ āđāļŦāđāļŠāļāļāļāļĨāđāļāļāļāļąāļāđāļāļāļāļēāļāđāļĨāļ°āļāđāļĒāļāļēāļĒāļāļāļāļāļĢāļīāļĐāļąāļ.
- āļĻāļķāļāļĐāļēāđāļĨāļ°āļāļīāļāļāļēāļĄāđāļāļ§āđāļāđāļĄāļāļēāļāđāļĻāļĢāļĐāļāļāļīāļ āļāļēāļĢāđāļāļīāļ āđāļĨāļ°āļāļĨāļēāļāļāļēāļĢāļĨāļāļāļļāļ āļāļąāđāļāđāļāđāļĨāļ°āļāđāļēāļāļāļĢāļ°āđāļāļĻ āđāļāļ·āđāļ āļāļģāļĄāļē āļāļĢāļ°āđāļĄāļīāļāđāļāļāļēāļŠāđāļĨāļ°āļāļ§āļēāļĄāđāļŠāļĩāđāļĒāļāļāļĩāđāļāļēāļāļ°āđāļāļīāļāļāļķāđāļ.
- āļ§āļēāļāđāļāļ āļāļģāļŦāļāļ āđāļĨāļ°āļāļĢāļąāļāļāļĨāļĒāļļāļāļāđāļāđāļēāļāļāļēāļĢāđāļāļīāļāđāļĨāļ°āļāļēāļĢāļĨāļāļāļļāļāđāļŦāđāđāļŦāļĄāļēāļ°āļŠāļĄāļāļąāļāđāļāđāļēāļŦāļĄāļēāļĒāļāļāļāļāļĢāļīāļĐāļąāļāļ āļēāļĒāđāļāđāļāļĢāļāļāļāļēāļĢāļāļĢāļīāļŦāļēāļĢāļāļ§āļēāļĄāđāļŠāļĩāđāļĒāļāļāļĩāđāļāļģāļŦāļāļ.
- āļāļĢāļīāļŦāļēāļĢāļāļĩāļĄāļāļēāļāļāđāļēāļāļāļēāļĢāļ§āļīāđāļāļĢāļēāļ°āļŦāđāđāļĨāļ°āļāļēāļĢāļĨāļāļāļļāļ āđāļŦāđāļŠāļēāļĄāļēāļĢāļāļāļāļīāļāļąāļāļīāđāļāđāļāļĒāđāļēāļāļĄāļĩāļāļĢāļ°āļŠāļīāļāļāļīāļ āļēāļ āđāļĨāļ°āļāļąāļāļāļēāļĻāļąāļāļĒāļ āļēāļāļāļĩāļĄāļāļēāļāđāļŦāđāļŠāļāļāļāļĨāđāļāļāļāļąāļāļāļīāļĻāļāļēāļāļāļāļāļāļāļāđāļāļĢ.
- āļāļĢāļ°āļŠāļēāļāļāļēāļāđāļĨāļ°āđāļŦāđāļāļģāļāļĢāļķāļāļĐāļēāđāļāđāļŦāļāđāļ§āļĒāļāļēāļāļāļĩāđāđāļāļĩāđāļĒāļ§āļāđāļāļ āđāļāļ·āđāļāļŠāļāļąāļāļŠāļāļļāļāļāļēāļĢāļāļģāđāļāļīāļāļāļēāļāļāđāļēāļāļāļēāļĢāđāļāļīāļāđāļĨāļ°āļāļēāļĢāļĨāļāļāļļāļāļāļāļāļāļĢāļīāļĐāļąāļ.
- āļāļāļīāļāļąāļāļīāļāļēāļāļāļ·āđāļ āđ āļāļĩāđāđāļāđāļĢāļąāļāļĄāļāļāļŦāļĄāļēāļĒāļāļēāļāļāļđāđāļāļąāļāļāļąāļāļāļąāļāļāļē.
- āļāļāļīāļāļąāļāļīāļāļēāļāļāļĒāđāļēāļāđāļāļāļĒāđāļēāļāļŦāļāļķāđāļāļŦāļĢāļ·āļāļāļąāđāļāļŦāļĄāļāļāļāļāļāļāļāļēāļāļāļēāļ āļāļąāļāļāļĩāđ.
- āļāļēāļĢāļ§āļīāđāļāļĢāļēāļ°āļŦāđāđāļĨāļ°āļāļąāļāļāļģāļĢāļēāļĒāļāļēāļāļāđāļēāļāļāļēāļĢāđāļāļīāļāđāļĨāļ°āļāļēāļĢāļĨāļāļāļļāļ.
- āļāļąāļāļāļģāđāļĨāļ°āļāļģāđāļŠāļāļ āļĢāļēāļĒāļāļēāļāļāļēāļĢāļĨāļāļāļļāļāļĢāļēāļĒāđāļāļ·āļāļ āļĢāļēāļĒāđāļāļĢāļĄāļēāļŠ āđāļĨāļ°āļĢāļēāļĒāļāļĩ āļāđāļāļāļđāđāļāļĢāļīāļŦāļēāļĢ.
- āļ§āļīāđāļāļĢāļēāļ°āļŦāđ āļāļĨāļāļāļāđāļāļāļāļēāļāļāļēāļĢāļĨāļāļāļļāļ āđāļĨāļ°āļāļāļāđāļāļĩāđāļĒāļĢāļąāļ āļĢāļ§āļĄāļāļķāļāļāļĢāļ°āļĄāļēāļāļāļēāļĢāļĨāđāļ§āļāļŦāļāđāļē 3-5 āļāļĩ.
- āļĻāļķāļāļĐāļēāđāļĨāļ°āļ§āļīāđāļāļĢāļēāļ°āļŦāđāđāļāļ§āđāļāđāļĄāļāļēāļāđāļĻāļĢāļĐāļāļāļīāļ āđāļāļ·āđāļāļāļēāļāļāļēāļĢāļāđāđāļāļ§āđāļāđāļĄāļāļāļāļāļĨāļēāļāđāļĨāļ°āļāļ§āļēāļĄāđāļāđāļāđāļāđāļāđāđāļāļāļēāļĢāļĨāļāļāļļāļ.
- āļ§āļīāđāļāļĢāļēāļ°āļŦāđāđāļĨāļ°āļŠāļĢāļļāļāļāđāļāļĄāļđāļĨāļāļēāļāļāļēāļĢāđāļāļīāļ āđāļāđāļ āļāļāļāļļāļĨ āļāļāļāļģāđāļĢāļāļēāļāļāļļāļ āđāļĨāļ°āļāļāļāļĢāļ°āđāļŠāđāļāļīāļāļŠāļ āđāļāļ·āđāļāļāļĢāļ°āđāļĄāļīāļāļŠāļāļēāļāļ°āļāļēāļāļāļēāļĢāđāļāļīāļāļāļāļāļāļĢāļīāļĐāļąāļ.
- āļāļģāļāļąāļāļāļđāđāļĨāļāļēāļĢāļāļąāļāļāļģāļĢāļēāļĒāļāļēāļāļāļēāļāļāļēāļĢāđāļāļīāļāđāļĨāļ°āļāļēāļĢāļĨāļāļāļļāļ āļĢāļ§āļĄāļāļķāļāļāļēāļĢāļ§āļīāđāļāļĢāļēāļ°āļŦāđāļāļĨāļāļāļāđāļāļāļāļēāļāļāļēāļĢāđāļāļīāļ.
- āļ§āļēāļāļāļĨāļĒāļļāļāļāđāļāđāļēāļāļāļēāļĢāļ§āļīāđāļāļĢāļēāļ°āļŦāđāļāđāļāļĄāļđāļĨāđāļĨāļ°āļāļēāļĢāļĨāļāļāļļāļ āđāļāļ·āđāļāđāļāļīāđāļĄāļāļĢāļ°āļŠāļīāļāļāļīāļ āļēāļāđāļĨāļ°āļāļĨāļāļāļāđāļāļāļāļāļāļāļĢāļīāļĐāļąāļ.
- āļ§āļīāđāļāļĢāļēāļ°āļŦāđāļāđāļāļĄāļđāļĨāđāļāļīāļāļĨāļķāļāđāļāļĩāđāļĒāļ§āļāļąāļāļāļāļāļēāļĢāđāļāļīāļ āđāļĨāļ°āļāļąāļāļāļģāļāļĢāļ°āļĄāļēāļāļāļēāļĢāļāļēāļāļāļēāļĢāđāļāļīāļāđāļĨāļ°āļāļēāļĢāļĨāļāļāļļāļ.
- āļĻāļķāļāļĐāļēāđāļāļ§āđāļāđāļĄāđāļĻāļĢāļĐāļāļāļīāļ āļāļēāļĢāđāļāļīāļ āđāļĨāļ°āļāļēāļĢāļĨāļāļāļļāļ āđāļāļ·āđāļāļāļģāļĄāļēāļāļĢāļ°āđāļĄāļīāļāļāļĨāļāļĢāļ°āļāļāđāļĨāļ°āđāļāļāļēāļŠāđāļāļāļēāļĢāļĨāļāļāļļāļāļāļāļāļāļĢāļīāļĐāļąāļ.
- āļāļīāļāļāļēāļĄāđāļĨāļ°āļāļĢāļąāļāļāļĨāļĒāļļāļāļāđāļāļēāļāļāļēāļĢāđāļāļīāļāđāļĨāļ°āļāļēāļĢāļĨāļāļāļļāļāđāļŦāđāļŠāļāļāļāļĨāđāļāļāļāļąāļāđāļāđāļēāļŦāļĄāļēāļĒāļāļāļāļāļĢāļīāļĐāļąāļ.
- āļāļēāļĢāļāļąāļāļāļĢāļ°āļāļļāļĄāļāļāļ°āļāļāļļāļāļĢāļĢāļĄāļāļēāļĢāļāļĢāļīāļŦāļēāļĢāļāļēāļĢāļĨāļāļāļļāļāđāļĨāļ°āļāļĢāļīāļŦāļēāļĢāļāļ§āļēāļĄāđāļŠāļĩāđāļĒāļāļāđāļēāļāļāļēāļĢāļĨāļāļāļļāļāļāļĒāđāļēāļāļāđāļāļĒāđāļāļĢāļĄāļēāļŠāļĨāļ°āļŦāļāļķāđāļāļāļĢāļąāđāļ āļŦāļĢāļ·āļāđāļĄāđāļāđāļāļĒāļāļ§āđāļēāļāļĩāļĨāļ°āļŠāļĩāđāļāļĢāļąāđāļ.
- āļāļĢāļīāļŦāļēāļĢāļāļĩāļĄāļāļēāļāļāļąāļāļ§āļīāđāļāļĢāļēāļ°āļŦāđāļāđāļāļĄāļđāļĨāđāļĨāļ°āļāļēāļĢāļĨāļāļāļļāļ āļĢāļ§āļĄāļāļķāļāļāļēāļĢāļāļąāļāļāļēāđāļĨāļ°āđāļŠāļĢāļīāļĄāļŠāļĢāđāļēāļāļĻāļąāļāļĒāļ āļēāļāļāļāļāļāļĩāļĄ.
- āļāļģāđāļŠāļāļāļĢāļēāļĒāļāļēāļāđāļĨāļ°āļāđāļāđāļŠāļāļāđāļāļ°āļāđāļēāļāļāļĨāļĒāļļāļāļāđāļāļēāļāļāļēāļĢāđāļāļīāļāđāļĨāļ°āļāļēāļĢāļĨāļāļāļļāļ āđāļāđāļāļđāđāļāļĢāļīāļŦāļēāļĢāļĢāļ°āļāļąāļāļŠāļđāļ.
- āļāļĢāļ°āļŠāļēāļāļāļēāļāļāļąāļāļŦāļāđāļ§āļĒāļāļēāļāļ āļēāļĒāđāļāđāļĨāļ°āļ āļēāļĒāļāļāļāļāļĩāđāđāļāļĩāđāļĒāļ§āļāđāļāļ āļĢāļ§āļĄāļāļķāļāļāļāļēāļāļēāļĢ āļŠāļāļēāļāļąāļāļāļēāļĢāđāļāļīāļ āļāļĢāļīāļĐāļąāļāļŦāļĨāļąāļāļāļĢāļąāļāļĒāđāļāļąāļāļāļēāļĢāļāļāļāļāļļāļ āļāļēāļĢāļ§āļīāđāļāļĢāļēāļ°āļŦāđāđāļĨāļ°āļāļąāļāļāļģāļĢāļēāļĒāļāļēāļāļāđāļēāļāļāļēāļĢāđāļāļīāļāđāļĨāļ°āļāļēāļĢāļĨāļāļāļļāļ.
- āļāļēāļĢāļ§āļīāđāļāļĢāļēāļ°āļŦāđāđāļĨāļ°āļāļąāļāļāļģāļĢāļēāļĒāļāļēāļ āļāļĢāļīāļĐāļąāļāļāļĩāđāļāļĢāļķāļāļĐāļēāļāļēāļĢāļĨāļāļāļļāļ āđāļĨāļ°āļŦāļāđāļ§āļĒāļāļēāļāļ āļēāļāļĢāļąāļāļ§āļīāđāļāļĢāļēāļ°āļŦāđāđāļĨāļ°āļāļąāļāļāļģāļĢāļēāļĒāļāļēāļāļāđāļēāļāļāļēāļĢāđāļāļīāļāđāļĨāļ°āļāļēāļĢāļĨāļāļāļļāļ.
- āļāļąāļāļāļģāđāļĨāļ°āļāļģāđāļŠāļāļ āļĢāļēāļĒāļāļēāļāļĨāļāļāļļāļāļĢāļēāļĒāđāļāļ·āļāļ āļĢāļēāļĒāđāļāļĢāļĄāļēāļŠ āđāļĨāļ°āļĢāļēāļĒāļāļĩ āļāđāļāļāļđāđāļāļĢāļīāļŦāļēāļĢ.
- āļ§āļīāđāļāļĢāļēāļ°āļŦāđ āļāļĨāļāļāļāđāļāļāļāļēāļāļāļēāļĢāļĨāļāļāļļāļ āđāļĨāļ°āļāļāļāđāļāļĩāđāļĒāļĢāļąāļ āļĢāļ§āļĄāļāļķāļāļāļĢāļ°āļĄāļēāļāļāļēāļĢāļĨāđāļ§āļāļŦāļāđāļē 3-5 āļāļĩ.
- āļĻāļķāļāļĐāļēāđāļĨāļ°āļ§āļīāđāļāļĢāļēāļ°āļŦāđāđāļāļ§āđāļāđāļĄāļāļēāļāđāļĻāļĢāļĐāļāļāļīāļ āđāļāļ·āđāļāļāļēāļāļāļēāļĢāļāđāđāļāļ§āđāļāđāļĄāļāļāļāļāļĨāļēāļāđāļĨāļ°āļāļ§āļēāļĄāđāļāđāļāđāļāđāļāđāđāļāļāļēāļĢāļĨāļāļāļļāļ.
- āļ§āļīāđāļāļĢāļēāļ°āļŦāđāđāļĨāļ°āļŠāļĢāļļāļāļāđāļāļĄāļđāļĨāļāļēāļāļāļēāļĢāđāļāļīāļ āđāļāđāļ āļāļāļāļļāļĨ āļāļāļāļģāđāļĢāļāļēāļāļāļļāļ āđāļĨāļ°āļāļāļāļĢāļ°āđāļŠāđāļāļīāļāļŠāļāđāļāļ·āđāļāļāļĢāļ°āđāļĄāļīāļāļŠāļāļēāļāļ°āļāļēāļāļāļēāļĢāđāļāļīāļāļāļāļāļāļĢāļīāļĐāļąāļ.
- āļāļēāļĢāļ§āļēāļāđāļāļāđāļĨāļāļāļģāļŦāļāļāļāļĨāļĒāļļāļāļāđāļāļēāļāļāļēāļĢāđāļāļīāļāđāļĨāļ°āļāļēāļĢāļĨāļāļāļļāļ.
- āļāļąāļāļāļģ Financial Forecast, Cashflow Projection āđāļĨāļ° Feasibility Study.
- āļ§āļēāļāđāļāļāđāļĨāļ°āļāļģāļŦāļāļ āļāļĨāļĒāļļāļāļāđāļāļēāļĢāļĨāļāļāļļāļ āđāļāļĒāļāđāļēāļāļāļīāļāļāļēāļāļāđāļāļĄāļđāļĨāđāļāļīāļāļĨāļķāļāđāļĨāļ°āđāļāļ§āđāļāđāļĄāļāļĨāļēāļ.
- āļāļīāļāļāļēāļĄāđāļĨāļ°āļāļĢāļąāļāļāļĢāļļāļāļāļĨāļĒāļļāļāļāđāļāļēāļāļāļēāļĢāđāļāļīāļāđāļĨāļ°āļāļēāļĢāļĨāļāļāļļāļ āđāļāļ·āđāļāđāļāļīāđāļĄāļĄāļđāļĨāļāđāļēāđāļĨāļ°āļĨāļāļāļ§āļēāļĄāđāļŠāļĩāđāļĒāļāļāļāļāļāļĢāļīāļĐāļąāļ.
- āļ§āļīāđāļāļĢāļēāļ°āļŦāđ S.W.O.T. (Strengths, Weakness, Opportunities, Threats) āđāļāļ·āđāļāļĢāļ°āļāļļāļāļļāļāđāļāđāļ āļāļļāļāļāđāļāļ āđāļāļāļēāļŠ āđāļĨāļ°āļāļ§āļēāļĄāđāļŠāļĩāđāļĒāļāļāļĩāđāļāļēāļāđāļāļīāļāļāļķāđāļ.
- āļāļēāļĢāļāļĢāļīāļŦāļēāļĢāļāļĩāļĄāđāļĨāļ°āļāļēāļĢāļāļģāļāļēāļāļĢāđāļ§āļĄāļāļąāļāļŦāļāđāļ§āļĒāļāļēāļāļāļĩāđāđāļāļĩāđāļĒāļ§āļāđāļāļ.
- āļāļĢāļīāļŦāļēāļĢāđāļĨāļ°āļāļąāļāļāļēāļāļĩāļĄāļ§āļīāđāļāļĢāļēāļ°āļŦāđāļāđāļāļĄāļđāļĨāđāļĨāļ°āļāļēāļĢāļĨāļāļāļļāļāđāļŦāđāļĄāļĩāļĻāļąāļāļĒāļ āļēāļāļŠāļđāļāļŠāļļāļ.
- āļāļĢāļ°āļŠāļēāļāļāļēāļāļāļąāļ āļŦāļāđāļ§āļĒāļāļēāļāļ āļēāļĒāđāļ āđāļāđāļ āļāđāļēāļĒāļāļąāļāļāļĩ āļāđāļēāļĒāļāļĢāļīāļŦāļēāļĢāļāļ§āļēāļĄāđāļŠāļĩāđāļĒāļ āđāļĨāļ°āļŦāļāđāļ§āļĒāļāļēāļāļāļĩāđāđāļāļĩāđāļĒāļ§āļāđāļāļ āđāļāļ·āđāļāđāļŦāđāļĄāļąāđāļāđāļāļ§āđāļēāļāļēāļĢāļ§āļīāđāļāļĢāļēāļ°āļŦāđāļāđāļāļĄāļđāļĨāđāļĨāļ°āļāļēāļĢāļĨāļāļāļļāļāđāļāđāļāđāļāļāļēāļĄāđāļāļāļāļĩāđāļāļģāļŦāļāļ.
- āļāļīāļāļāđāļāđāļĨāļ°āļāļģāļāļēāļāļĢāđāļ§āļĄāļāļąāļ āļŦāļāđāļ§āļĒāļāļēāļāļ āļēāļĒāļāļāļ āđāļāđāļ āļŠāļāļēāļāļąāļāļāļēāļĢāđāļāļīāļ āļāļĢāļ·āļĐāļąāļāļāļĩāđāļāļĢāļķāļāļĐāļēāļāļēāļĢāļĨāļāļāļļāļ āđāļĨāļ°āļŦāļāđāļ§āļĒāļāļēāļāļāļģāļāļąāļāļāļđāđāļĨ.
- āļāļēāļĢāļāļīāļāļāđāļāļāđāļēāļ§āļŠāļēāļĢāđāļĨāļ°āļāļąāļāļāļąāļĒāļ āļēāļĒāļāļāļāļāļĩāđāļŠāđāļāļāļĨāļāđāļāļāļĢāļīāļĐāļąāļ.
- āļāļīāļāļāļēāļĄ āđāļāļ§āđāļāđāļĄāđāļĻāļĢāļĐāļāļāļīāļ āļāļĨāļēāļāļāļēāļĢāđāļāļīāļ āđāļĨāļ°āļāđāļĒāļāļēāļĒāļ āļēāļāļĢāļąāļ āļāļĩāđāļāļēāļāļŠāđāļāļāļĨāļāđāļāļāļĨāļĒāļļāļāļāđāļāļēāļĢāļĨāļāļāļļāļāļāļāļāļāļĢāļīāļĐāļąāļ.
- āļ§āļīāđāļāļĢāļēāļ°āļŦāđ āļāļąāļāļāļąāļĒāļ āļēāļĒāđāļāđāļĨāļ°āļ āļēāļĒāļāļāļ āļāļĩāđāļāļēāļāļāļĢāļ°āļāļāļāđāļāļāļĢāļīāļĐāļąāļ āļāļąāđāļāđāļāđāļāļīāļāļāļ§āļāđāļāļīāļāļĨāļ āļāļĢāđāļāļĄāļāļģāđāļŠāļāļāđāļāļ§āļāļēāļāļĢāļąāļāļĄāļ·āļ.
- āļŠāļāļąāļāļŠāļāļļāļāļāļēāļāļāļ·āđāļ āđ āļāļēāļĄāļāļĩāđāđāļāđāļĢāļąāļāļĄāļāļāļŦāļĄāļēāļĒ āđāļāļ·āđāļāđāļŦāđāļŦāļāđāļ§āļĒāļāļēāļāđāļĨāļ°āļāļĢāļīāļĐāļąāļ āđāļāļĒāđāļĨāļāļāđ āļāļĢāļīāļ§āļīāđāļĨāļ āļāļēāļĢāđāļ āļāļģāļāļąāļāļ āļēāļĢāļāļīāļāļāļĩāđāļāļģāļŦāļāļ.
- āđāļāđāļĢāļąāļāļāļĢāļīāļāļāļēāļāļĢāļĩāļŦāļĢāļ·āļāļāļļāļāļ§āļļāļāļīāļāļĒāđāļēāļāļāļ·āđāļāļāļĩāđāđāļāļĩāļĒāļāđāļāđāļĢāļ°āļāļąāļāđāļāļĩāļĒāļ§āļāļąāļāđāļāļŠāļēāļāļēāļ§āļīāļāļēāđāļ āļŠāļēāļāļēāļ§āļīāļāļēāļŦāļāļķāđāļ āļāļēāļāļāļēāļĢāđāļāļīāļ āļāļąāļāļāļĩ āļāļĢāļīāļŦāļēāļĢāļāļļāļĢāļāļīāļ āđāļĻāļĢāļĐāļāļŠāļēāļŠāļāļĢāđ.
- āļĄāļĩāļāļĢāļ°āļŠāļāļāļēāļĢāļāđāļāđāļēāļāļāļēāļĢāļ§āļīāđāļāļĢāļēāļ°āļŦāđāļāļēāļĢāđāļāļīāļ āļāļēāļĢāļĨāļāļāļļāļ āļāļēāļĢāļāļĢāļīāļŦāļēāļĢāļŠāļīāļāļāļĢāļąāļāļĒāđāļŦāļĢāļ·āļāļāļēāļāļāļĩāđāđāļāļĩāđāļĒāļ§āļāđāļāļāļāļĒāđāļēāļāļāđāļāļĒ 7-10 āļāļĩ āđāļĨāļ°āļāļĢāļ°āļŠāļāļāļēāļĢāļāđāļĢāļ°āļāļąāļāļāļĢāļīāļŦāļēāļĢ 3-5 āļāļĩ.
- āļĄāļĩāļāļ§āļēāļĄāļĢāļđāđāļāđāļēāļāļāļēāļĢāļ§āļīāđāļāļĢāļēāļ°āļŦāđāļāļāļāļēāļĢāđāļāļīāļ āļāļēāļĢāļāļĢāļīāļŦāļēāļĢāļāļāļĢāđāļāļāļēāļĢāļĨāļāļāļļāļ āļāļēāļĢāļāļąāļāļāļģāļāļĢāļ°āļĄāļēāļāļāļēāļĢāļāļēāļāļāļēāļĢāđāļāļīāļāđāļĨāļ°āļāļēāļĢāļāļĢāļ°āđāļĄāļīāļāļĄāļđāļĨāļāđāļēāđāļāļĢāļāļāļēāļĢ.
- āļĄāļĩāļāļ§āļēāļĄāđāļāđāļēāđāļāđāļāļāļēāļĢāļāļĨāļēāļāļāļļāļ āļāļĨāļēāļāđāļāļīāļ āđāļāļĢāļ·āđāļāļāļĄāļ·āļāļāļēāļĢāļĨāļāļāļļāļāļāļąāđāļāđāļāļāļĢāļ°āđāļāļĻāđāļĨāļ°āļāđāļēāļāļāļĢāļ°āđāļāļĻ.
- āļĄāļĩāļāļąāļāļĐāļ°āļāļēāļĢāļāļĢāļīāļŦāļēāļĢāļāļąāļāļāļēāļĢāļāļĩāļĄ āļāļēāļĢāļŠāļ·āđāļāļŠāļēāļĢ āđāļĨāļ°āļāļēāļĢāļāļĢāļ°āļŠāļēāļāļāļēāļāļāļĩāđāļāļĩ.
- āļĄāļĩāļāļąāļāļĐāļ°āļāļēāļĢāļāļĢāļīāļŦāļēāļĢāļāļąāļāļāļēāļĢāļāļĩāļĄ āļāļēāļĢāļŠāļ·āđāļāļŠāļēāļĢ āđāļĨāļ°āļāļēāļĢāļāļĢāļ°āļŠāļēāļāļāļēāļāļāļĩāđāļāļĩ.
- āļŠāļēāļĄāļēāļĢāļāđāļāđāđāļāđāļāļĢāđāļāļĢāļĄāļ§āļīāđāļāļĢāļēāļ°āļŦāđāļāđāļāļĄāļđāļĨāđāļĨāļ°āļāļīāļāļāđāđāļ§āļĢāđāļāļēāļāļāļēāļĢāđāļāļīāļāđāļāđ (āđāļāđāļ SETSMART, SETTRADE Streaming, Bisnews, ThaiBMA Bloomberg, Reuters āļāļ°āļāļīāļāļēāļĢāļāļēāđāļāđāļāļāļīāđāļĻāļĐ).
- āļĄāļĩāļāļąāļāļĐāļ°āļāļēāļĢāļāļīāļāđāļāļīāļāļāļĨāļĒāļļāļāļāđ āļāļēāļĢāļ§āļīāđāļāļĢāļēāļ°āļŦāđāđāļāļīāļāļāļĢāļīāļĄāļēāļ āđāļĨāļ°āļāļēāļĢāđāļāđāđāļāļāļąāļāļŦāļē.
- āļāļ§āļēāļĄāļĢāļđāđ āļāļąāļāļĐāļ° āđāļĨāļ°āļŠāļĄāļĢāļĢāļāļ°āļāļĩāđāļāļģāđāļāđāļāđāļāļāļēāļ.
- āļĄāļĩāļāļĢāļ°āļŠāļāļāļēāļĢāļāđāļāđāļēāļāļāļēāļĢāļ§āļīāđāļāļĢāļēāļ°āļŦāđāļāđāļāļĄāļđāļĨāļāļēāļāļāļēāļĢāđāļāļīāļāđāļĨāļ°āļāļēāļĢāļĨāļāļāļļāļ āđāļĄāđāļāđāļāļĒāļāļ§āđāļē 7-10 āļāļĩ.
- āļĄāļĩāļāļĢāļ°āļŠāļāļāļēāļĢāļāđāđāļāļāļģāđāļŦāļāđāļ āļāļđāđāļāļąāļāļāļēāļĢ āļŦāļĢāļ·āļ āļāļđāđāļāļĢāļīāļŦāļēāļĢāļĢāļ°āļāļąāļāļŠāļđāļ āļāļĒāđāļēāļāļāđāļāļĒ 3 āļāļĩ.
- āļĄāļĩāļāļąāļāļĐāļ° āļāļēāļĢāļ§āļīāđāļāļĢāļēāļ°āļŦāđāļāđāļāļĄāļđāļĨāļāļąāđāļāļŠāļđāļ āđāļĨāļ°āļŠāļēāļĄāļēāļĢāļāđāļāđāđāļāļĢāđāļāļĢāļĄ Excel, Power BI āļŦāļĢāļ·āļāļāļāļāļāđāđāļ§āļĢāđāļāļĩāđāđāļāļĩāđāļĒāļ§āļāđāļāļāđāļāđāļāļĩ.
- āļĄāļĩāļāļ§āļēāļĄāđāļāđāļēāđāļāđāļāļĩāđāļĒāļ§āļāļąāļ Financial Modeling, Cashflow Management, āđāļĨāļ° Risk Management.
- āļĄāļĩāļāļ§āļēāļĄāļŠāļēāļĄāļēāļĢāļāđāļāļāļēāļĢāļāļģāđāļŠāļāļāļāđāļāļĄāļđāļĨāđāļāļīāļāļāļĨāļĒāļļāļāļāđāļāļĢāļīāļŦāļēāļĢāļĢāļ°āļāļąāļāļŠāļđāļ.
- āļĄāļĩāļāļąāļāļĐāļ°āļāļēāļĢāļāļĢāļīāļŦāļēāļĢāļāļĩāļĄ āđāļĨāļ°āļŠāļēāļĄāļēāļĢāļāļāļģāļāļēāļāļĢāđāļ§āļĄāļāļąāļāļŦāļāđāļ§āļĒāļāļēāļāļāļĩāđāđāļāļĩāđāļĒāļ§āļāđāļāļāđāļāđāļāļĒāđāļēāļāļĄāļĩāļāļĢāļ°āļŠāļīāļāļāļīāļ āļēāļ.
- āļĄāļĩāļāļąāļāļĐāļ°āļ āļēāļĐāļēāļāļąāļāļāļĪāļĐāđāļāļāļēāļĢāļāļīāļāļāđāļāļŠāļ·āđāļāļŠāļēāļĢāđāļāđāļĢāļ°āļāļąāļāļāļĩ.
- āļāļēāļĄāļāļĢāļīāļĐāļąāļ āđāļāļĒāđāļĨāļāļāđ āļāļĢāļīāļ§āļīāđāļĨāļ āļāļēāļĢāđāļ āļāļģāļāļąāļ āļāļĢāļ°āļāļēāļĻāļāļēāļĄāļāļģāđāļŦāļāđāļāļāļēāļ.
āļāļąāļāļĐāļ°:
SQL, Tableau, Power BI
āļāļĢāļ°āđāļ āļāļāļēāļ:
āļāļēāļāļāļĢāļ°āļāļģ
āđāļāļīāļāđāļāļ·āļāļ:
āļŠāļēāļĄāļēāļĢāļāļāđāļāļĢāļāļāđāļāđ
- Data Cleaning and Preparation - Need to retrieve data from one or more sources and prepare the data so it is ready for numerical and categorical analysis. Data cleaning also involves handling missing and inconsistent data that may affect your analysis.
- Data Analysis and Exploration - Take a business question or need and turn it into a data question. Then, transform and analyze data to extract an answer to that question. Moreover, find interesting trends or relationships in the data that could bring value to a business.
- Creating Data Visualizations and Communication - Produce reports or build dashboards on your findings and communicate to business stakeholders and managements.
- Statistical Knowledge.
- Mathematical Ability.
- Programming languages, such as SQL.
- Analytic tools such as Tableau, Power BI.
- TeraData, Big data Hadoop Tech, Cloud Tech.
- Bachelor Degrees in MIS, Business, Economic, Computer Science or related field.
- At least 2-3 year of experience with Data Analysis.
- Experienced in designing and architecture BI / Data Analytics Solutions is preferred.
āļāļąāļāļĐāļ°:
Product Development
āļāļĢāļ°āđāļ āļāļāļēāļ:
āļāļēāļāļāļĢāļ°āļāļģ
āđāļāļīāļāđāļāļ·āļāļ:
āļŠāļēāļĄāļēāļĢāļāļāđāļāļĢāļāļāđāļāđ
- Establish and enforce enterprise-level data governance policies, standards, and controls to support data integrity, security, and lifecycle management..
- Collaborate with stakeholders across business units to define data ownership, stewardship, and accountability models.
- Advise on Data Governance Operating Model, Cloud Governance, and Security Governance to support the client s digital transformation roadmap..
- Digital investment.
- IT infrastructure.
- Application governance.
- Cybersecurity.
- Data & AI.
- Digital product development.
- Facilitate effective data collection, storage, access, and usage aligned with industry best practices and regulatory requirements.
- Lead data stewardship initiatives ensuring consistent data quality, metadata management, and data lifecycle processes.
- Provide expert advisory on governance implications across business areas and propose solutions for cross-team challenges.
- Support cloud governance management and implementation aligned with enterprise architecture and security standards.
- 7-12 years of experience in Data Governance, IT Governance, or Technology Strategy roles..
- Expert proficiency in IT Governance (required)..
- Strong proficiency in Data Governance and governance operating model design (recommended)..
- Experience in Cloud Governance, Security Governance, and digital operating model development (preferred)..
- Strong stakeholder engagement and influencing skills, with the ability to advise senior leaders.
- Experience working on large-scale digital transformation programs is an advantage.
- Strong analytical, problem-solving, and communication skills..
āļāļĢāļ°āđāļ āļāļāļēāļ:
āļāļēāļāļāļĢāļ°āļāļģ
āđāļāļīāļāđāļāļ·āļāļ:
āļŠāļēāļĄāļēāļĢāļāļāđāļāļĢāļāļāđāļāđ
- āļāļāļīāļāļąāļāļīāļāļēāļāļĒāļąāļ āļāļĢāļīāļĐāļąāļ Infinitas by Krungthai.
- We are seeking a highly skilled Data Management Specialist to join our team. The ideal candidate will be responsible for managing and analyzing large data, ensuring data integrity, and developing data management solutions to optimize our operations.
- Design, develop, and maintain data mart and pipeline to ensure accessibility and reliability of data.
- Utilize SQL to perform complex queries, data extraction, and manipulation.
- Analyze and interpret large datasets to identify trends and insights that drive business decisions.
- Ensure data quality, integrity, and security across all managed databases.
- Collaborate with cross-functional teams to understand data needs and deliver effective data management
- solutions.
- Implement and monitor data management policies and procedures.
- Optimize database performance and troubleshoot issues as they arise.
- Leverage big data technologies such as Spark to process and analyze large datasets.
- Develop and maintain ETL processes to ensure seamless data flow between systems.
- Work with cloud platforms such as Google Cloud and AWS to manage and store data securely.
- Required Skills and Qualifications
- Proficiency in SQL: Expertise in writing and optimizing complex SQL queries.
- Problem-solving and analytical skills: Strong ability to analyze data and derive actionable insights.
- Familiarity with cloud platforms: Experience with platforms such as Google Cloud and AWS.
- Database management systems: Hands-on experience with systems like MySQL.
- Big data technologies: Experience with technologies such as Spark.
- Python programming: Proficiency in Python for data manipulation and analysis.
- Strong communication and collaboration skills to work effectively with cross-functional teams.
- Attention to detail and commitment to maintaining high data quality standards.
- Ability to manage multiple projects and prioritize tasks in a fast-paced environment.
- Education & Experience
- Bachelor s degree or higher in Statistics, Computer Science, Mathematics, or related field
- Minimum 3 years experience in retail lending or similar role
- Additional Requirements
- Experience with data visualization tools (Tableau, Power BI)
- Ability to work collaboratively with cross-functional teams.
- You have read and reviewed Infinitas By Krungthai Company Limited's Privacy Policy at https://krungthai.com/Download/download/DownloadDownload_73Privacy_Policy_Infinitas.pdf. The Bank does not intend or require the processing of any sensitive personal data, including information related to religion and/or blood type, which may appear on copy of your identification card. Therefore, please refrain from uploading any documents, including copy(ies) of your identification card, or providing sensitive personal data or any other information that is unrelated or unnecessary for the purpose of applying for a position on the website. Additionally, please ensure that you have removed any sensitive personal data (if any) from your resume and other documents before uploading them to the website.
- The Bank is required to collect your criminal record information to assess employment eligibility, verify qualifications, or evaluate suitability for certain positions. Your consent to the collection, use, or disclosure of your criminal record information is necessary for entering into an agreement and being considered for the aforementioned purposes. If you do not consent to the collection, use, or disclosure of your criminal record information, or if you later withdraw such consent, the Bank may be unable to proceed with the stated purposes, potentially resulting in the loss of your employment opportunity with. ".
āļāļĢāļ°āđāļ āļāļāļēāļ:
āļāļēāļāļāļĢāļ°āļāļģ
āđāļāļīāļāđāļāļ·āļāļ:
āļŠāļēāļĄāļēāļĢāļāļāđāļāļĢāļāļāđāļāđ
- āļāļāļīāļāļąāļāļīāļāļēāļāļĒāļąāļ āļāļĢāļīāļĐāļąāļ Infinitas by Krungthai
- We seek a skilled Data Scientist to enhance banking through advanced analytics and machine learning. Main responsibility will be transformed complex data into actionable insights and models for risk management, fraud detection, and customer experience optimization. Collaborate with cross-functional teams to implement data-driven strategies that improve business performance and risk management.
- Data Analysis and Modeling
- Develop predictive models for risk assessment and market analysis.
- Design machine learning algorithms for classification, regression and fraud detection.
- Create automated systems for customer behavior analysis.
- Strategic Leadership
- Identify opportunities for data-driven solutions with stakeholders.
- Present analytical findings to executive management.
- Education and Experience
- Bachelor s Degree or higher in Computer Science, Statistics, Mathematics, Engineer or related field.
- Minimum 2 years of experience in financial data science.
- Technical Skills
- Proficiency in Python, and SQL.
- Strong data analysis skills.
- Solid understanding of statistical analysis and modeling techniques.
- Excellent problem-solving skills and attention to detail.
- Strong communication skills, with the ability to explain complex concepts to non-technical stakeholders.
- Domain Knowledge
- Understanding financial markets and banking operations.
- Financial modeling and quantitative analysis expertise.
- Understanding basic fraud detection and anomaly detection.
- You have read and reviewed Infinitas By Krungthai Company Limited's Privacy Policy at https://krungthai.com/Download/download/DownloadDownload_73Privacy_Policy_Infinitas.pdf. The Bank does not intend or require the processing of any sensitive personal data, including information related to religion and/or blood type, which may appear on copy of your identification card. Therefore, please refrain from uploading any documents, including copy(ies) of your identification card, or providing sensitive personal data or any other information that is unrelated or unnecessary for the purpose of applying for a position on the website. Additionally, please ensure that you have removed any sensitive personal data (if any) from your resume and other documents before uploading them to the website.
- The Bank is required to collect your criminal record information to assess employment eligibility, verify qualifications, or evaluate suitability for certain positions. Your consent to the collection, use, or disclosure of your criminal record information is necessary for entering into an agreement and being considered for the aforementioned purposes. If you do not consent to the collection, use, or disclosure of your criminal record information, or if you later withdraw such consent, the Bank may be unable to proceed with the stated purposes, potentially resulting in the loss of your employment opportunity with.".
āļāļąāļāļĐāļ°:
Digital Marketing, Big Data, Statistics
āļāļĢāļ°āđāļ āļāļāļēāļ:
āļāļēāļāļāļĢāļ°āļāļģ
āđāļāļīāļāđāļāļ·āļāļ:
āļŠāļēāļĄāļēāļĢāļāļāđāļāļĢāļāļāđāļāđ
- Drive clear and effective business translation of AI/ML products between business and technical stakeholders.
- Design, develop and leverage Advanced analytics, Artificial Intelligence (AI) and Machine Learning (ML) models to support digital marketing, MarTech, AdTech, and hyper-personalization initiatives.
- Analyze Big Data to develop effective predictive and recommendation models.
- Collaborate closely with Product Owners, IT teams, and Data teams to implement AI solutions that improve marketing campaign performance.
- Continuously refine and enhance AI models through testing and performance evaluation.
- Participate in the vendor selection processes to identify and ensure the best external partners for data science and AI/MLprojects..
- Bachelor s Degree or higher in Computer Science, Computer Engineering, Data Science, Statistics, or any related field.
- Minimum of 2 years in AI/ML engineer, cloud solution or a related field.
- Proficiency in some of the following: Python, PySpark and SQL etc.
- Experience or strong interest in digital marketing, MarTech, and AdTech, especially data-driven marketing strategies is a plus.
- Experience in building tools / models to support retention, up-cross selling, optimization, mobile app data and digital marketing is a plus.
- Ability to communicate and collaborate with cross-functional teams.
- Growth mindset and openness to continuously learning and facing new projects and new technologies..
- You have read and reviewed Infinitas By Krungthai Company Limited's Privacy Policy at https://krungthai.com/Download/download/DownloadDownload_73Privacy_Policy_Infinitas.pdf. The Bank does not intend or require the processing of any sensitive personal data, including information related to religion and/or blood type, which may appear on copy of your identification card. Therefore, please refrain from uploading any documents, including copy(ies) of your identification card, or providing sensitive personal data or any other information that is unrelated or unnecessary for the purpose of applying for a position on the website. Additionally, please ensure that you have removed any sensitive personal data (if any) from your resume and other documents before uploading them to the website.
- The Bank is required to collect your criminal record information to assess employment eligibility, verify qualifications, or evaluate suitability for certain positions. Your consent to the collection, use, or disclosure of your criminal record information is necessary for entering into an agreement and being considered for the aforementioned purposes. If you do not consent to the collection, use, or disclosure of your criminal record information, or if you later withdraw such consent, the Bank may be unable to proceed with the stated purposes, potentially resulting in the loss of your employment opportunity with.".
āļāļąāļāļĐāļ°:
Power BI, Tableau, Finance, English
āļāļĢāļ°āđāļ āļāļāļēāļ:
āļāļēāļāļāļĢāļ°āļāļģ
āđāļāļīāļāđāļāļ·āļāļ:
āļŠāļēāļĄāļēāļĢāļāļāđāļāļĢāļāļāđāļāđ
- Collect, clean, and analyze data from sales, marketing, and CRM systems.
- Build and maintain dashboards and performance reports (Power BI, Tableau, or Google Data Studio).
- Monitor and evaluate campaign results, product performance, and sales trends.
- Provide data-driven insights to support marketing strategies and business planning.
- Collaborate with sales, finance, and product teams to align business insights.
- Ensure data accuracy and consistency across all reports.
- Bachelor s degree in Business Analytics, Data Science, Statistics, Economics, or related field.
- At least 3 years of experience in data analysis, business intelligence, or commercial analytics.
- Strong skills in Excel, SQL, and data visualization tools (Power BI, Tableau, or Google Data Studio).
- Experience with CRM and marketing analytics tools is a plus.
- Analytical, detail-oriented, and comfortable presenting insights to management.
- Good command of English and Thai, both written and spoken.
āļāļąāļāļĐāļ°:
Budgeting, Procurement, Excel
āļāļĢāļ°āđāļ āļāļāļēāļ:
āļāļēāļāļāļĢāļ°āļāļģ
āđāļāļīāļāđāļāļ·āļāļ:
āļŋ45,000 - āļŋ60,000, āļŠāļēāļĄāļēāļĢāļāļāđāļāļĢāļāļāđāļāđ
- Support the annual and quarterly budgeting process by collecting and consolidating data from multiple teams.
- Monitor and analyze monthly spending to ensure the division stays within budget and follows company policies.
- Prepare and reconcile accrual budgets and ensure accuracy between planned vs. actual expenses.
- Review and control PR/PO issuance to support responsible spending.
- Improve and manage procurement workflows, office supply control, and standardized operating processes.
- Maintain accurate inventories of assets, software licenses, and equipment used in the division.
- Coordinate with internal and external stakeholders on budgeting, reporting, and operational matters.
- Ensure new hires receive complete and timely office equipment and tools.
- Use advanced Excel, Power Query, and data analytics tools to automate reports and provide financial insights..
- Bachelor s degree in Accounting, Business Administration, Finance, Economics, or related field.
- At least 3 years of experience in accounting, budgeting, financial operations, or related roles..
- Strong knowledge of accounting principles, expense control, and basic procurement processes.
- Solid understanding of loan or leasing businesses is a plus.
- Advanced Excel (PivotTable, Power Query, XLOOKUP, automation).
- Experience with data analysis tools.
- Strong analytical and problem-solving abilities with great attention to detail.
- Good interpersonal skills, service-minded, adaptable, and proactive.
āļāļąāļāļĐāļ°:
Sales, Negotiation, Software Development, English
āļāļĢāļ°āđāļ āļāļāļēāļ:
āļāļēāļāļāļĢāļ°āļāļģ
āđāļāļīāļāđāļāļ·āļāļ:
āļŠāļēāļĄāļēāļĢāļāļāđāļāļĢāļāļāđāļāđ
- Provide technical consultancy and design solutions for Enterprise Customers, primarily focusing on Enterprise Data Service products.
- Support the Sales team with technical opportunities, prepare technical proposals, and respond to customer requirements.
- Take ownership of customer solutions and architecture design, including solution costing.
- Collaborate with vendors, providers, and partners to optimize project investment costs for enhanced competitiveness.
- Coordinate and hand over customer solutions to the delivery and operation teams.
- Coordinate with vendors and partners to explore new potential technologies.
- Share technical and service knowledge with internal stakeholders.
- Bachelor of Engineering (Computer, IT, Telecommunication) or Computer Science.
- 5-8 years of experience in IT, Telecommunication, Data Communication Service, Pre-Sales, Post-Sales, or IP Network Operations & Planning.
- At least 5 years of professional experience as a Pre-Sales Engineer in a technical environment.
- Excellent presentation and negotiation skills.
- Strong analytical skills, excellent interpersonal and communication skills.
- Fluency in English is preferable.
- Strong knowledge and experience in Optical, DWDM, MPLS, and Routing are preferable.
- Extensive experience in the ISP, Internet peering, and International connectivity industry is preferable.
- Relevant software development experience or commercial/sales experience.
- Ability to understand and determine an enterprise customer's needs and how AIS's products and solutions might best fit through a consultative approach.
- Ability to thrive in a fast-paced environment, set demanding expectations, and consistently exceed them.
- Must be organized, a self-starter, and capable of delivering high-quality work without constant tactical oversight.
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°:
ETL, Power BI, SQL
āļāļĢāļ°āđāļ āļāļāļēāļ:
āļāļēāļāļāļĢāļ°āļāļģ
āđāļāļīāļāđāļāļ·āļāļ:
āļŠāļēāļĄāļēāļĢāļāļāđāļāļĢāļāļāđāļāđ
- āļĢāļąāļāļāļīāļāļāļāļāļāļēāļĢāļāļāļāđāļāļ Data model āļāļĩāđāļĢāļāļāļĢāļąāļāļāļēāļĢāđāļāđāļāļēāļāļĢāļ°āļĒāļ°āļĒāļēāļ§.
- āļāđāļ§āļĒāļ§āļēāļāđāļāļāļāļēāļĢāļŠāļĢāđāļēāļ Data mart āđāļŦāđāđāļŦāļĄāļēāļ°āļāļąāļ use case āļāđāļēāļāđ.
- āļāļģāļāļēāļāļĢāđāļ§āļĄāļāļąāļāļāļĩāļĄ ETL / Data Engineer āđāļāļ·āđāļāļāļąāļ schema, pipeline āđāļŦāđāļāļĢāļāļāļąāļāļāļ§āļēāļĄāļāđāļāļāļāļēāļĢ.
- āļŠāļ·āđāļāļŠāļēāļĢāđāļĨāļ°āđāļāļĨ requirement āļāļļāļĢāļāļīāļāļĄāļēāđāļāđāļ solution āđāļāļīāļāļāđāļāļĄāļđāļĨāđāļāđāļāļĩ.
- āļāļģāļŦāļāđāļēāļāļĩāđāļ§āļīāđāļāļĢāļēāļ°āļŦāđāļāđāļāļĄāļđāļĨāđāļāļīāļāļĨāļķāļāđāļŦāđāļāļąāļāļŦāļāđāļ§āļĒāļāļļāļĢāļāļīāļ.
- āļāļāļāđāļāļāđāļĨāļ°āļāļąāļāļāļē Dashboard / Report āļāļ Power BI āļāļĒāđāļēāļ advance.
- āļāļķāļāļāđāļāļĄāļđāļĨāļāļēāļ DWH āđāļāļĒāđāļāđ SQL āļāļĩāđāļāļąāļāļāđāļāļ āļĢāļ§āļĄāļāļķāļāļāļēāļĢāļāļģ data wrangling.
- āļĄāļĩāļāļĢāļ°āļŠāļāļāļēāļĢāļāđāļāļģāļāļēāļāđāļāļŠāļēāļĒāļāļēāļ Data (BA āļŦāļĢāļ·āļ SA) 5 āļāļĩ.
- āļĄāļĩāļāļ§āļēāļĄāđāļāļĩāđāļĒāļ§āļāļēāļāļ āļēāļĐāļē SQL āđāļāđāļāļāļĒāđāļēāļāļāļĩ.
- āļĄāļĩāļāļ§āļēāļĄāđāļāļĩāđāļĒāļ§āļāļēāļ Visualization Tool āđāļāđāļ Power BI āđāļĨāļ°āļŦāļĢāļ·āļ Data tools āļāļ·āđāļāđāļāļĩāđāđāļāļĩāđāļĒāļ§āļāļąāļ Data.
- āļĄāļĩāļāļ§āļēāļĄāļĢāļđāđāļāļ§āļēāļĄāđāļāđāļēāđāļāđāļ Data Warehouse Concept, ETL āđāļāđāļāļāļĒāđāļēāļāļāļĩ.
- āļāļąāļāļĐāļ°āļāļēāļĢāļāļāļāđāļāļ Data model, Data Mart.
- āļāļąāļāļĐāļ°āđāļāļāļēāļĢāļ§āļīāđāļāļĢāļēāļ°/āđāļāđāđāļāļāļąāļāļŦāļē.
- Contact Information:-.
- K. Sawarin Tel.
- Office of Human Capital.
- DIGITAL AND TECHNOLOGY SERVICES CO., LTD.
- F.Y.I Center 2525 Rama IV Rd, Khlong Tan, Khlong Toei, Bangkok 10110.
- MRT QSNCC Station Exit 1.
āļāļĢāļ°āļŠāļāļāļēāļĢāļāđ:
2 āļāļĩāļāļķāđāļāđāļ
āļāļąāļāļĐāļ°:
Business Statistics / Analysis, English
āļāļĢāļ°āđāļ āļāļāļēāļ:
āļāļēāļāļāļĢāļ°āļāļģ
āđāļāļīāļāđāļāļ·āļāļ:
āļŋ27,100 - āļŋ35,000, āļŠāļēāļĄāļēāļĢāļāļāđāļāļĢāļāļāđāļāđ
- To communicate and coordinate with all channels of sales team to re-confirm output and training..
- To activate/implement special project; to monitor operation and define the key success factor..
- Planning and monitoring all sales operation in TT channel of sales system smoothly..
- Work closely with the team to understand their data needs and provide actionable insights..
- Initiate, design big picture by canvas tools to support team..
- Job Qualification.
- Bachelor's Degree in Business Administration, Computer Science or related fields..
- Have at least 1-3 years' experience of Support sales team and design BI dashboard..
- Strong in BI Dashboard and Data Analysis and can design visualize presentation..
- Able to clearly communication with internal and external, easy-to-understand and actionable way..
- Able manage multiple projects simultaneously with attention to detail..
- Works well with cross-functional teams, stays flexible and solution-oriented in dynamic environments..
- Stays positive and solutions-focused under pressure..
- Fluency in spoken & written English..
āļāļąāļāļĐāļ°:
Power BI, Python, SQL
āļāļĢāļ°āđāļ āļāļāļēāļ:
āļāļēāļāļāļĢāļ°āļāļģ
āđāļāļīāļāđāļāļ·āļāļ:
āļŠāļēāļĄāļēāļĢāļāļāđāļāļĢāļāļāđāļāđ
- Power BI, Python, SQL Query.
- Experienced in managing Big Data - 10 million+ Rows.
- Experienced in FMCG is preferrable.
- āļ§āļīāđāļāļĢāļēāļ°āļŦāđāļāđāļāļĄāļđāļĨāļāļēāļāļ āļēāļĒāđāļāđāļĨāļ°āļ āļēāļĒāļāļāļāļāļāļāđāļāļĢ (Internal & External Data Analysis) āđāļāļĒāļāļēāļĢāđāļāđāļāļĢāļ§āļāļĢāļ§āļĄāđāļĨāļ°āļ§āļīāđāļāļĢāļēāļ°āļŦāđāļāđāļāļĄāļđāļĨāļāļĩāđāļŠāđāļāļāļĨāļāļĢāļ°āļāļāļāđāļāļāļāļāđāļāļĢ āļāļēāļāļŦāļĨāļēāļĒāđāļŦāļĨāđāļ āļāļąāđāļāļ āļēāļĒāđāļāđāļĨāļ°āļ āļēāļĒāļāļāļ āļāļąāđāļāđāļāļāđāļēāļ Finance & Non Finance āļĢāļ§āļĄāļāļķāļ āđāļŦāļāļļāļāļēāļĢāļāđāļāļĩāđāđāļāļīāļāļāļķāđāļāļāļąāļāļāļēāļāļāļ°āļāļĢāļ°āļāļāļāļ§āļēāļĄāđāļāđāļāļāļĒāļđāđāļāļāļāļāļāļąāļāļāļēāļ.
- āļ§āļīāđāļāļĢāļēāļ°āļŦāđāļāļĪāļāļīāļāļĢāļĢāļĄ/āđāļŦāļāļļāļāļēāļĢāļāđ āđāļĨāļ°āļāļēāļāļāļēāļĢāļāđāđāļāļāļēāļŠāļŦāļĢāļ·āļāļāļĨāļāļąāļāļāļēāļāļāļ°āļāļĢāļ°āļāļāļāđāļāļāļĢāļīāļĐāļąāļāđāļĨāļ°āļāļāļąāļāļāļēāļ.
- āļāļāļāđāļāļāđāļĨāļ°āļāļģāļāļēāļĢāļāļāļŠāļāļāļ§āļīāļāļĩāđāļāđāļāļēāļāļāđāļāļĄāļđāļĨāđāļĨāļ°āđāļāļāļāļīāļāļāļēāļĢāļ§āļīāđāļāļĢāļēāļ°āļŦāđāđāļŦāļĄāđāđ āđāļāļ·āđāļāļāļģāđāļŠāļāļāļĄāļļāļĄāļĄāļāļāļāļēāļāļāļļāļĢāļāļīāļāđāļŦāļĄāđāđāđāļŦāđāđāļāđāļāļđāđāļāļĩāđāđāļāļĩāđāļĒāļ§āļāđāļāļ.
- āļāļąāļāļāļģāļĢāļēāļĒāļāļēāļāļŠāļĢāļļāļāļāđāļāļĄāļđāļĨāļāļĩāđāđāļāđāļēāđāļāļāđāļēāļĒ āđāļĨāļ°āļŠāļĢāđāļēāļ Dashboard āđāļŦāđāļāļđāđāļāļĢāļīāļŦāļēāļĢ.
- āļāļģāđāļŠāļāļāļāļĨāļāļēāļĢāļ§āļīāđāļāļĢāļēāļ°āļŦāđāđāļāļ·āđāļāļāļĢāļ°āļāļāļāļāļēāļĢāļāļąāļāļŠāļīāļāđāļāđāļāļīāļāļāļĨāļĒāļļāļāļāđ.
- āļāļēāļĢāļŠāļĢāđāļēāļ āļāļđāđāļĨ āđāļĨāļ° āļāļąāļāđāļāļāļāļēāļāļāđāļāļĄāļđāļĨ āđāļĨāļ°āļāļēāļĢāļāļĢāļ°āļĄāļ§āļĨāļāļĨāļāđāļāļĄāļđāļĨāļāļāļēāļāđāļŦāļāđ.
- āļāļąāļāļāļēāđāļĨāļ°āđāļāđāļāļēāļāļĢāļ°āļāļāļāļąāļāđāļāļĄāļąāļāļī āđāļāļāļēāļĢāļāļģāļāļēāļāļāļąāļāļāļēāļāļāđāļāļĄāļđāļĨ.
- āļāļēāļāļāļ·āđāļāđāļāļĩāđāđāļāļĩāđāļĒāļ§āļāđāļāļāđāļāļ·āđāļāļŠāļāļąāļāļŠāļāļļāļāļāļēāļĢāļāļģāļāļēāļāļāļāļāļāļĩāļĄ.
- āļāļģāđāļŦāļāđāļāļāļēāļāļāļĩāđāļāļģāđāļāđāļāļāđāļāļāļāđāļēāļāļāļēāļĢāļāļĢāļ§āļāļŠāļāļāļāļĢāļ°āļ§āļąāļāļīāļāļēāļāļāļēāļāļĢāļĢāļĄāļāļēāļĄāļŦāļĨāļąāļāđāļāļāļāđāļāļĩāđāļāļĢāļīāļĐāļąāļāļāļģāļŦāļāļ ***.
āļāļąāļāļĐāļ°:
Data Analysis
āļāļĢāļ°āđāļ āļāļāļēāļ:
āļāļēāļāļāļĢāļ°āļāļģ
āđāļāļīāļāđāļāļ·āļāļ:
āļŠāļēāļĄāļēāļĢāļāļāđāļāļĢāļāļāđāļāđ
- āļāļēāļĢāļ§āļīāđāļāļĢāļēāļ°āļŦāđāļāđāļāļĄāļđāļĨ (Data Analysis).
- āļāļģāļŦāļāļāđāļŦāļĨāđāļāļāđāļāļĄāļđāļĨāđāļĨāļ°āļĢāļ§āļāļĢāļ§āļĄāļāđāļāļĄāļđāļĨāļāļēāļāđāļŦāļĨāđāļāļāđāļēāļāđ.
- Cleaning āļāđāļāļĄāļđāļĨāļāļĢāļ§āļāļŠāļāļāļāļ§āļēāļĄāļāļđāļāļāđāļāļ āđāļĨāļ° āļāļąāļāļāļēāļĢāļāđāļāļĄāļđāļĨāļāļĩāđāļĒāļąāļāđāļĄāđāļāļĢāļāļāđāļ§āļāđāļŦāđāļŠāļĄāļāļđāļĢāļāđ.
- āļ§āļīāđāļāļĢāļēāļ°āļŦāđāļāđāļāļĄāļđāļĨāđāļāļĒāđāļāđāļŠāļāļīāļāļī, āđāļāļĢāļ·āđāļāļāļĄāļ·āļāļ§āļīāđāļāļĢāļēāļ°āļŦāđ āđāļĨāļ°āļŠāļĢāđāļēāļāļ āļēāļāļāđāļāļĄāļđāļĨ (Data Visualization) āđāļāļ·āđāļāļāđāļāļŦāļēāđāļāļ§āđāļāđāļĄāđāļĨāļ°āļāđāļāļĄāļđāļĨāđāļāļīāļāļĨāļķāļ.
- āļŠāļĢāđāļēāļāļĢāļēāļĒāļāļēāļāđāļĨāļ°āđāļāļāļāļāļĢāđāļ (Dashboards) āđāļāļ·āđāļāļāļģāđāļŠāļāļāļāļĨāļāļēāļĢāļ§āļīāđāļāļĢāļēāļ°āļŦāđāđāļŦāđāļāļąāļāļāļđāđāļāļĢāļīāļŦāļēāļĢāđāļĨāļ°āļāļĩāļĄāļāļēāļ.
- āļ§āļīāđāļāļĢāļēāļ°āļŦāđāļāļĪāļāļīāļāļĢāļĢāļĄāļĨāļđāļāļāđāļē, āļĢāļđāļāđāļāļāļāļēāļĢāļāļēāļĒ āļŦāļĢāļ·āļāļāļĢāļ°āļāļ§āļāļāļēāļĢāļāļģāļāļēāļāļāļāļāļŦāļāđāļ§āļĒāļāļēāļ Product āļĢāļ§āļĄāļāļķāļāļāļĨāļļāđāļĄāļĨāļđāļāļāđāļē āđāļāļ·āđāļāđāļāđāļāļąāļāļŦāļēāđāļĨāļ°āļŦāļēāđāļāļ§āļāļēāļāļāļĢāļąāļāļāļĢāļļāļ.
- āļāļēāļĢāļāļąāļāļāļēāđāļĨāļ°āļāļĢāļąāļāļāļĢāļļāļāļāļĢāļ°āļāļ§āļāļāļēāļĢāļāļģāļāļēāļ (Process Development) āđāļāļĒāđāļāđāļāđāļāļĄāļđāļĨāđāļāļīāļāļĨāļķāļāļāļĩāđāđāļāđāļāļēāļāļāļēāļĢāļ§āļīāđāļāļĢāļēāļ°āļŦāđāļĄāļēāļāđāļ§āļĒāđāļāđāđāļāļāļąāļāļŦāļēāđāļĨāļ°āļāļĢāļąāļāļāļĢāļļāļāļāļĢāļ°āļāļ§āļāļāļēāļĢāļāļģāļāļēāļāđāļŦāđāļĄāļĩāļāļĢāļ°āļŠāļīāļāļāļīāļ āļēāļāļĄāļēāļāļāļķāđāļ.
- āļāļąāļāļāļēāļāļĢāļ°āļāļ§āļāļāļēāļĢāļŦāļĢāļ·āļāđāļāļĢāļ·āđāļāļāļĄāļ·āļāđāļāļāļēāļĢāđāļāđāļāļĢāļ§āļāļĢāļ§āļĄāđāļĨāļ°āđāļāđāļāđāļāļĄāļđāļĨāđāļŦāđāđāļāļīāļāļāļĢāļ°āđāļĒāļāļāđāļŠāļđāļāļŠāļļāļ.
- āļĢāđāļ§āļĄāļāļąāļāļāļēāļĢāļ°āļāļāļĢāļēāļĒāļāļēāļāđāļŦāđāļŠāļāļāļāļĨāđāļāļāļāļąāļāļāļ§āļēāļĄāļāđāļāļāļāļēāļĢāļāļāļāđāļāđāļĨāļ°āļŠāđāļ§āļāļāļēāļāđāļāļāļāļāđāļāļĢ.
- Specification.
- āļāļēāļĒāļļ 35 āļāļĩ āļāļķāđāļāđāļ.
- āļāļĢāļīāļāļāļēāļāļĢāļĩ āļŠāļēāļāļēāļāļĩāđāđāļāļĩāđāļĒāļ§āļāđāļāļ.
- āļĄāļĩāļāļĢāļ°āļŠāļāļāļēāļĢāļāđāđāļāļāļēāļĢāļāļģāļāļēāļāļāđāļēāļāļ§āļīāđāļāļĢāļēāļ°āļŦāđāļāđāļāļĄāļđāļĨ āļāļąāļāļāļēāļĢāļđāļāđāļāļ āļĄāļēāļāļĢāļāļēāļāļāļēāļĢāļāļāļīāļāļąāļāļīāļāļēāļāļāļāļ Operation āļāļāļāļāļēāļāļāļĩāđ āļāļēāļĢāļ§āļīāđāļāļĢāļēāļ°āļŦāđāļāļąāļāļŦāļē āđāļĨāđāļ§āļāļģāļĄāļēāļāļģāļŦāļāļāļĢāļđāļāđāļāļ āđāļāļ§āļāļēāļāļāļēāļĢāļāļāļīāļāļąāļāļīāļāļēāļāđāļŦāļĄāđāđ āļāļēāļĢāļŠāļ·āđāļāļŠāļēāļĢ āļāļēāļĢāđāļŦāđāļāļģāđāļāļ°āļāļģāļāļđāđāļāļĩāđāđāļāļĩāđāļĒāļ§āļāđāļāļ āļāļĢāđāļāļĄāļāļĨāļąāļāļāļąāļāđāļŦāđāđāļāļīāļāļāļēāļĢāđāļāļĨāļĩāđāļĒāļāđāļāļĨāļāđāļāđāļāļĒāđāļēāļāđāļāđāļāļĢāļđāļāļāļĢāļĢāļĄ āļ§āļąāļāļāļĨāđāļāđ.
- āļāļĢāļ°āļŠāļāļāļēāļĢāļāđāđāļāļāļēāļĢāļāļģāļāļēāļ āļāļĒāđāļēāļāļāđāļāļĒ 3 āļāļĩāļāļķāđāļāđāļ.
- āļŦāļēāļāļĄāļĩāļāļĢāļ°āļŠāļāļāļēāļĢāļāđāļāļĢāļāļāļđāđāļĨāļāļēāļāļāđāļēāļāļāļēāļĢāļ§āļīāđāļāļĢāļēāļ°āļŦāđāļāļēāļ Operation āļāļ°āļāļīāļāļēāļĢāļāļēāđāļāđāļāļāļīāđāļĻāļĐ.
- āļĄāļĩāļāļ§āļēāļĄāđāļāđāļāļāļđāđāļāļģ āļĄāļļāđāļāļĄāļąāđāļ āļāļāļāļāļēāļāļāļēāļĒāđāļĨāļ°āļāļēāļāļāļĢāļīāļāļēāļĢ āļāļąāđāļāļĨāļđāļāļāđāļēāļ āļēāļĒāđāļ āđāļĨāļ°āļ āļēāļĒāļāļāļ.
- āļĄāļĩāļāļąāļāļĐāļ°āļāļēāļĢāļŠāļ·āđāļāļŠāļēāļĢ āđāļāđāļĄāļāđāļēāļ§ āđāļāļĢāļāļē āđāļāđāļāļąāļāļŦāļēāđāļāļāļēāļ°āļŦāļāđāļēāđāļāđāļāļĩ.
- Key Competencies.
- āļĄāļĩāļāļ§āļēāļĄāļŠāļēāļĄāļēāļĢāļāđāļāļāļēāļĢāđāļĢāļĩāļĒāļāļĢāļđāđ āļāļ§āļēāļĄāļŦāļĨāļēāļāļŦāļĨāļēāļĒāļāļāļāļĢāļđāļāđāļāļāļŠāļīāļāļāđāļēāđāļĨāļ°āļāļĢāļīāļāļēāļĢāđāļŦāļĄāđāđ āđāļāđāļāļĩ.
- āļāļ§āļēāļĄāļŠāļēāļĄāļēāļĢāļāđāļāļāļēāļĢāļāļģāđāļŠāļāļ āđāļāđāļĄāļāđāļēāļ§ āđāļāļ·āđāļāđāļŦāđāđāļāļīāļāļāļēāļĢāđāļāļĨāļĩāđāļĒāļāđāļāļĨāļ.
- āļāļ§āļēāļĄāļŠāļēāļĄāļēāļĢāļāđāļāļāļēāļĢāļāļīāļāļāļēāļĄāļāļēāļ āļĢāļ§āļĄāđāļāļāļķāļāļāđāļ§āļĒāđāļŦāļĨāļ·āļāļāļĩāļĄāļāļēāļ āđāļāļ·āđāļāļĢāļāļāļĢāļąāļāļāļēāļĢāđāļāļĨāļĩāđāļĒāļāđāļāļĨāļ āđāļĨāļ°āđāļŦāđāļāļĢāļīāļāļēāļĢāļĨāļđāļāļāđāļēāđāļāđāđāļāđāļāļāļĒāđāļēāļāļāļĩ.
- āļāļģāļāļēāļāļ āļēāļĒāđāļāđāļ āļēāļ§āļ°āļāļ§āļēāļĄāļāļāļāļąāļāđāļĨāļ°āđāļāđāđāļāļāļąāļāļŦāļēāđāļāļāļēāļ°āļŦāļāđāļēāđāļāđāļāļĩ.
- āļĄāļĩāļāļąāļĻāļāļāļāļīāļāļĩāđāļāļĩ āđāļĨāļ°āļāļēāļĢāļāļģāļāļēāļāļĢāđāļ§āļĄāļāļąāļāļāļąāļāļāļĩāļĄāļāļēāļāļāļĩāđāđāļāļĩāđāļĒāļ§āļāđāļāļāđāļāļ·āđāļāļāļ§āļēāļĄāļŠāļģāđāļĢāđāļāļāļāļāļāļēāļ.
āļāļąāļāļĐāļ°:
Quality Assurance, Assurance, Data Analysis, English
āļāļĢāļ°āđāļ āļāļāļēāļ:
āļāļēāļāļāļĢāļ°āļāļģ
āđāļāļīāļāđāļāļ·āļāļ:
āļŠāļēāļĄāļēāļĢāļāļāđāļāļĢāļāļāđāļāđ
- Perform daily operational tasks such as quality inspection, guidelines and process optimization, queue assignment, and handling escalations for the evaluation project.
- Work closely with stakeholders to stay updated on guideline developments and provide feedback on implementation and execution.
- Conduct daily audits on an internal system and provide error analysis and feedback to stakeholders (R&D & Product Manager).
- Monitor the quality scores of evaluators and conduct root cause analysis with the management team.
- Monitor project data, record daily output and quality scores, prepare data analysis/reports for projects, and validate reports and data provided to stakeholders/partners.
- Localize the guidelines and design training schedules and coordinate and liaise with key stakeholders to ensure the successful go live within the targeted timeframe.
- Deliver process/product/guideline training to new joiners.
- Play a role in setting up product knowledge tests, sharing result analysis, and working with key stakeholders to improve the product knowledge of the team.
- Completion of Bachelor's degree or above.
- Proficiency in English and Thai as working languages.
- 1 year of experience in Quality Analyst/Quality Assurance, particularly in search engine evaluation.
- Familiarity with search engines, social media algorithms, and SEO.
- Demonstrated computer proficiency with Office software.
- Deep understanding of local culture and internet usage habits.
- Attention to detail and ability to use data analysis to identify trends.
āļāļąāļāļĐāļ°:
Power BI, Tableau, SQL
āļāļĢāļ°āđāļ āļāļāļēāļ:
āļāļēāļāļāļĢāļ°āļāļģ
āđāļāļīāļāđāļāļ·āļāļ:
āļŠāļēāļĄāļēāļĢāļāļāđāļāļĢāļāļāđāļāđ
- Data Lifecycle Management: A Data PM oversees the entire lifecycle of data projects, from data acquisition, integration, and storage to analysis and visualization. This involves significant understanding of technical processes and data systems.
- Collaboration with Technical Teams: Work closely with engineers, data scientists, and IT teams to ensure data pipelines and infrastructures align with project goals. This requires a deep understanding of technical jargon, workflows, and dependencies.
- Monitoring and Reporting: Track project progress and provide regular updates and rep ...
- Mastery of Technical Tools and Platforms.
- BI Tools: Power BI, Tableau.
- Project & Code Collaboration: GitHub, JIRA, Confluence.
- Cloud Systems: AWS, Azure, Google Cloud for data solutions.
- The role often requires working knowledge of SQL, Python, or other languages to interpret project outcomes, test processes, and validate results.
- Technical Decision-Making Authority.
- System Architecture: A Data PM may decide how data systems should be architected or what infrastructure to adopt based on project requirements.
- Tool Selection: Selecting appropriate analytics tools, databases, or platforms for project success is a regular part of the role.
- Ensuring Data Integrity: Data governance, accuracy, and validation are all technical concerns within a Data PM s purview.
- Challenges of the Data PM Role.
- Translate business needs into data requirements.
- Collaborate meaningfully with technical teams on implementation.
- Ensure compliance with technical standards (e.g., data security, privacy).
- Bachelor's or master's degree in a relevant field, such as Data Science, Computer Science, Business Administration, or Supply Chain Management.
- 3-5 years of experience in Project Management or a similar role, preferably in Data or IT domains.
- Strong understanding of Retail, Wholesale, or Supply Chain processes.
- Proficient in project management tools like Jira, Trello, or Microsoft Project.
- Experience with Agile/Scrum methodologies.
- Familiarity with.
- Data Analytics: SQL, Python, or other languages to interpret project outcomes.
- Data Engineering: Data pipelines, ETL processes, data storage systems.
- Data Science: Algorithms, machine learning models, statistical analysis, A/B testing.
- Technical Roadblocks: Anticipating and resolving issues like system integration, latency, and scalability.
- Excellent communication and stakeholder management skills.
- Proficient in English communication.
- 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.".
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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
