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āļāļĢāļ°āļŠāļāļāļēāļĢāļāđ:
2 āļāļĩāļāļķāđāļāđāļ
āļāļąāļāļĐāļ°:
Python, SQL, Tableau, Quantitative Analysis
āļāļĢāļ°āđāļ āļāļāļēāļ:
āļāļēāļāļāļĢāļ°āļāļģ
āđāļāļīāļāđāļāļ·āļāļ:
āļŠāļēāļĄāļēāļĢāļāļāđāļāļĢāļāļāđāļāđ
- Understanding of business problems forgathering/analyzing/designing the data to solve the problems from end-to-end process.
- Excellent in written and verbal communication skills for coordinating across teams.
- Analyze large amounts of information to discover trends and patterns.
- Lead the analytics group in management of projects and experiment designing, implementing innovative Predictive Models, implementing innovative analytic solutions or driving outstanding results.
- Proficient in common data science toolkits (R, Python) and using SQL languages.
- Experience using business intelligence tools (e.g. Tableau) and data frameworks (e.g. Hadoop).
- Good applied statistics skills, such as distributions, statistical testing, regression, etc.
- At least 2-3 year in Business Intelligence/Data Analytic.
- Proficiency in statistical analysis, quantitative analytic, forecasting/predictive analytic, experimental design and optimization algorithms.
- Technical expertise regarding data modeling, data mining, segmentation techniques, unstructured data skills, and other data science.
- Programming skills in big data frameworks and statistical modeling such as SAS, R, and Python, CE Techniques.
- Experience in data visualization tools.
- Knowledge/experience in machine learning.
- Domain knowledge of mobile, network and telecommunication technology and services would be advantageous.
āļāļąāļāļĐāļ°:
ETL, DevOps, Automation
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°:
Sales, Marketing Strategy, Statistics
āļāļĢāļ°āđāļ āļāļāļēāļ:
āļāļēāļāļāļĢāļ°āļāļģ
āđāļāļīāļāđāļāļ·āļāļ:
āļŠāļēāļĄāļēāļĢāļāļāđāļāļĢāļāļāđāļāđ
- Analyze consumer behavior, market trends, and competitor data, and provide data-driven recommendations for business expansion or marketing strategy development.
- Collect sales, operational, and marketing data from internal and external sources to assess the impact of marketing campaigns and forecast business opportunities.
- Update and maintain accurate databases, and create analytical reports (Dashboards) to present data to management.
- Coordinate with marketing, sales, and other departments to gather information for analysis and address the needs of the organization.
- Bachelor's degree or higher in Data Science, Marketing, Statistics, or a related field.
- At least 2-3 years of experience in data analysis. Experience in retail business will be considered an advantage.
- Proficiency in data analysis tools such as Excel, SQL, Power BI, Tableau, or Python.
- Strong analytical skills and the ability to solve problems in a systematic manner.
- Good presentation and communication skills to convey complex information clearly.
- Attention to detail and the ability to work under pressure.
āļāļąāļāļĐāļ°:
Power BI, Excel, Microsoft Office
āļāļĢāļ°āđāļ āļāļāļēāļ:
āļāļēāļāļāļĢāļ°āļāļģ
āđāļāļīāļāđāļāļ·āļāļ:
āļŠāļēāļĄāļēāļĢāļāļāđāļāļĢāļāļāđāļāđ
- āļ§āļīāđāļāļĢāļēāļ°āļŦāđ āđāļĨāļ°āļāļąāļāļāļģāļĢāļēāļĒāļāļēāļāđāļāļĢāļĩāļĒāļāđāļāļĩāļĒāļāļāđāļāļĄāļđāļĨ āļāđāļēāļāđ āđāļāđāļ āļĢāļēāļĒāļ§āļąāļ, āļĢāļēāļĒāđāļāļ·āļāļ, āļĢāļēāļĒāļāļĩ āđāļāļĒāđāļāļĢāļĩāļĒāļāđāļāļĩāļĒāļāļāļąāļāļāđāļāļĄāļđāļĨāđāļāļāļāļĩāļāđāļāđāļēāļŦāļĄāļēāļĒ āđāļĨāļ°āļāļēāļĢāļāļĢāļ°āļĄāļēāļāļāļēāļĢāđāļāļāļāļēāļāļ āđāļāļ·āđāļāļāļģāđāļŠāļāļāļāļđāđāļāļĢāļīāļŦāļēāļĢ.
- āļāļąāļāļāļģāļāļāļāļĢāļ°āļĄāļēāļāļāļĢāļ°āļāļģāļāļĩāđāļāļŠāđāļ§āļāļāļāļ āļĒāļāļāļāļēāļĒ āđāļĨāļ°āļāđāļēāđāļāđāļāđāļēāļĒāļŠāđāļāđāļŠāļĢāļīāļĄāļāļēāļĢāļāļēāļĒ.
- āļāļąāļāļāļģāļĢāļēāļĒāļāļēāļāļŠāļĢāļļāļ Profit & Loss āļāļĢāļ°āļāļģāđāļāļ·āļāļāđāļāļĄāļļāļĄāļĄāļāļāļāļĢāļīāļŦāļēāļĢāļŊ āđāļāļ·āđāļāļāļģāđāļŠāļāļāļāļđāđāļāļĢāļīāļŦāļēāļĢ.
- āļāļąāļāļāļģāļāļēāļāļāđāļāļĄāļđāļĨāļāļēāļĄāđāļāļ§āļāļēāļāļāđāļēāļāđ āđāļāļ·āđāļāļŠāļāļąāļāļŠāļāļļāļ āļāļēāļĢāļāļģāļāļ§āļ/āļāļēāļĢāļāļģāļĢāļēāļĒāļāļēāļāļāļāļāļŠāđāļ§āļāļāļēāļāļāļ·āđāļāđ āļāļĩāđāđāļāļĩāđāļĒāļ§āļāđāļāļ āđāļāđāļ āđāļāđāļēāļŦāļĄāļēāļĒāļāļēāļĢāļāđāļēāļĒāđāļāļīāļāļāļđāļāđāļāđāļŦāđāļāļąāļāļāļāļąāļāļāļēāļ, āđāļāđāļēāļŦāļĄāļēāļĒāļāļēāļĢāļāđāļēāļĒāđāļāļīāļāļĢāļēāļāļ§āļąāļĨ āđāļāđāļāļāđāļ.
- āļāļāļāđāļāļ Dashboard āļŠāļģāļŦāļĢāļąāļāļāđāļāļĄāļđāļĨāļāđāļēāļāļāđāļēāļāđ āļāļĩāđāđāļāļĩāđāļĒāļ§āļāđāļāļ āđāļŦāđāļāļđāđāđāļāđāļāļēāļāđāļāđāļēāđāļ āđāļĨāļ°āļŠāļēāļĄāļēāļĢāļāļāļģāđāļāđāļāđāđāļāđāļāđāļēāļĒ āđāļāđāļ Power BI.
- āļāļāļāđāļāļ Template āđāļŦāđāļŠāđāļ§āļāļāļēāļāļāđāļēāļāđāļāļĩāđāđāļāļĩāđāļĒāļ§āļāđāļāļ āđāļāļ·āđāļāļĢāļāļāļĢāļąāļāļāļēāļĢāļāļģāļāļēāļāđāļāļāļēāļĢāļĢāļ§āļāļĢāļ§āļĄāļāđāļāļĄāļđāļĨ āđāļāļ·āđāļāļāđāļ§āļĒāđāļŦāđāļāļēāļĢāļāļģāļāļēāļ āđāļāđāļāļĨāļĨāļąāļāļāđāļāļĩāđāļĢāļ§āļāđāļĢāđāļ§āļāļķāđāļ.
- āļ§āļīāđāļāļĢāļēāļ°āļŦāđāļāđāļāļĄāļđāļĨāļāļ·āđāļ āđ āļāļĩāđāđāļāļĩāđāļĒāļ§āļāđāļāļ āļāļēāļĄāļāļĩāđāđāļāđāļĢāļąāļāļĄāļāļāļŦāļĄāļēāļĒ.
- āđāļŦāđāļāļģāđāļāļ°āļāļģāđāļāļāļēāļ§āļīāđāļāļĢāļēāļ°āļŦāđāļāđāļāļĄāļđāļĨ āđāļāđāļŦāļāđāļ§āļĒāļāļēāļ āļāđāļēāļ āđ āļāļĩāđāļĄāļĩāļāļēāļĢāđāļāđāļāļēāļāļāđāļāļĄāļđāļĨ.
- āļāļēāļāļāļ·āđāļ āđ āļāļĩāđāđāļāđāļĢāļąāļāļĄāļāļāļŦāļĄāļēāļĒ.
- āļĄāļĩāļāļąāļāļĐāļ°āđāļāļāļēāļĢāđāļāđāļāļāļĄāļāļīāļ§āđāļāļāļĢāđāđāļāļĢāđāļāļĢāļĄ MS ExcelāļāļąāđāļāļŠāļđāļ.
- āļĄāļĩāļāļąāļāļĐāļ°āļāļēāļĢāđāļāđāļāļāļĄāļāļīāļ§āđāļāļāļĢāđāļāļ·āđāļāđ āđāļāđāđāļāđāļāļāļĒāđāļēāļāļāļĩ: Microsoft Office; Word, Power Point.
- āļĄāļĩāļāļ§āļēāļĄāļŠāļēāļĄāļēāļĢāļāļāļēāļāļāļēāļĢāļ§āļīāđāļāļĢāļēāļ°āļŦāđ āļāļēāļĢāļ§āļēāļāđāļāļ āđāļĨāļ°āļāļēāļĢāļāļąāļāļāļēāļĢāļāļĒāđāļēāļāđāļāđāļāļĢāļ°āļāļ āđāļĨāļ°āļĄāļĩāļĄāļēāļāļĢāļāļēāļāđāļāļāļēāļĢāļāļģāļāļēāļ.
- āļĄāļĩāļāļąāļāļĐāļ°āđāļāļāļēāļĢāļŠāļ·āđāļāļŠāļēāļĢ āđāļĨāļ°āļāļ§āļēāļĄāļŠāļēāļĄāļēāļĢāļ āđāļāļāļēāļĢāđāļāļĢāļāļē āļāđāļāļĢāļāļ.
- āļĄāļĩāļāļ§āļēāļĄāļŠāļēāļĄāļēāļĢāļāđāļāļāļēāļĢāđāļĢāļĩāļĒāļāļĢāļđāđāļŠāļīāđāļāđāļŦāļĄāđ āđ āđāļāđāļĢāļ§āļāđāļĢāđāļ§.
- āļĄāļĩāļāļąāļāļĐāļ°āđāļāļāļēāļĢāļāļģāđāļŠāļāļāļāļēāļ.
- āļĄāļĩāļāļąāļāļĐāļ°āđāļāļāļēāļĢāđāļāđ Power BI (āļāđāļēāļĄāļĩ).
- āļĄāļĩāļāļ§āļēāļĄāļĢāļđāđāļāļ·āđāļāļāļēāļāļāđāļēāļāļāļēāļĢāđāļāļĩāļĒāļāđāļāļĢāđāļāļĢāļĄāļāđāļēāļ āđ (āļāđāļēāļĄāļĩ).
- āļāļīāļāļāđāļāļŠāļāļāļāļēāļĄ.
- āļāļĢāļīāļĐāļąāļ āđāļĄāđāļāļīāļĢāđāļāđāļāļĢāļ āđāļĄāļāđāļāļāđāļĄāđāļāļāđ āļāļģāļāļąāļ.
- āļāļēāļāļēāļĢāđāļĨāđāļēāđāļāđāļāļāđāļ§āļ 1 āļāļąāđāļ 26 āļāļāļāļ§āļīāļ āļēāļ§āļāļĩāļĢāļąāļŠāļīāļ āđāļāļ§āļāļāļāļĄāļāļĨ āđāļāļāļāļāļļāļāļąāļāļĢ āđāļāļ§āļāļāļāļĄāļāļĨ āđāļāļāļāļāļļāļāļąāļāļĢ āļāļąāļāļŦāļ§āļąāļāļāļĢāļļāļāđāļāļāļĄāļŦāļēāļāļāļĢ.
āļāļĢāļ°āļŠāļāļāļēāļĢāļāđ:
1 āļāļĩāļāļķāđāļāđāļ
āļāļąāļāļĐāļ°:
Statistics, Data Analysis, Finance
āļāļĢāļ°āđāļ āļāļāļēāļ:
āļāļēāļāļāļĢāļ°āļāļģ
āđāļāļīāļāđāļāļ·āļāļ:
āļŠāļēāļĄāļēāļĢāļāļāđāļāļĢāļāļāđāļāđ
- Bachelor s degree in Statistics, Economics, Mathematics, or related field.
- 1-3 years of experience in data analysis or related roles. Experience in banking or finance preferred.
- Proficiency in Excel, SQL, Python/R, data visualization tools like Tableau or Power BI. Strong statistical analysis skills.
- Strong understanding of data analysis, statistical methods, and business insights. Knowledge of user personas and journey mapping in data-centric roles.
- Contact: [email protected] (K.Thipwimon).
- āļāđāļēāļāļŠāļēāļĄāļēāļĢāļāļāđāļēāļāđāļĨāļ°āļĻāļķāļāļĐāļēāļāđāļĒāļāļēāļĒāļāļ§āļēāļĄāđāļāđāļāļŠāđāļ§āļāļāļąāļ§āļāļāļāļāļāļēāļāļēāļĢāļāļĢāļļāļāđāļāļĒ āļāļģāļāļąāļ (āļĄāļŦāļēāļāļ) āļāļĩāđ https://krungthai.com/th/content/privacy-policy āļāļąāđāļāļāļĩāđ āļāļāļēāļāļēāļĢāđāļĄāđāļĄāļĩāđāļāļāļāļēāļŦāļĢāļ·āļāļāļ§āļēāļĄāļāļģāđāļāđāļāđāļāđ āļāļĩāđāļāļ°āļāļĢāļ°āļĄāļ§āļĨāļāļĨāļāđāļāļĄāļđāļĨāļŠāđāļ§āļāļāļļāļāļāļĨāļāļĩāđāļĄāļĩāļāļ§āļēāļĄāļāđāļāļāđāļŦāļ§ āļĢāļ§āļĄāļāļķāļāļāđāļāļĄāļđāļĨāļāļĩāđāđāļāļĩāđāļĒāļ§āļāđāļāļāļĻāļēāļŠāļāļēāđāļĨāļ°/āļŦāļĢāļ·āļāļŦāļĄāļđāđāđāļĨāļŦāļīāļ āļāļķāđāļāļāļēāļāļāļĢāļēāļāļāļāļĒāļđāđāđāļāļŠāļģāđāļāļēāļāļąāļāļĢāļāļĢāļ°āļāļģāļāļąāļ§āļāļĢāļ°āļāļēāļāļāļāļāļāļāđāļēāļāđāļāđāļāļĒāđāļēāļāđāļ āļāļąāļāļāļąāđāļ āļāļĢāļļāļāļēāļāļĒāđāļēāļāļąāļāđāļŦāļĨāļāđāļāļāļŠāļēāļĢāđāļāđ āļĢāļ§āļĄāļāļķāļāļŠāļģāđāļāļēāļāļąāļāļĢāļāļĢāļ°āļāļģāļāļąāļ§āļāļĢāļ°āļāļēāļāļ āļŦāļĢāļ·āļāļāļĢāļāļāļāđāļāļĄāļđāļĨāļŠāđāļ§āļāļāļļāļāļāļĨāļāļĩāđāļĄāļĩāļāļ§āļēāļĄāļāđāļāļāđāļŦāļ§āļŦāļĢāļ·āļāļāđāļāļĄāļđāļĨāļāļ·āđāļāđāļ āļāļķāđāļāđāļĄāđāđāļāļĩāđāļĒāļ§āļāđāļāļāļŦāļĢāļ·āļāđāļĄāđāļāļģāđāļāđāļāļŠāļģāļŦāļĢāļąāļāļ§āļąāļāļāļļāļāļĢāļ°āļŠāļāļāđāđāļāļāļēāļĢāļŠāļĄāļąāļāļĢāļāļēāļāđāļ§āđāļāļāđāļ§āđāļāđāļāļāđ āļāļāļāļāļēāļāļāļĩāđ āļāļĢāļļāļāļēāļāļģāđāļāļīāļāļāļēāļĢāđāļŦāđāđāļāđāđāļāļ§āđāļēāđāļāđāļāļģāđāļāļīāļāļāļēāļĢāļĨāļāļāđāļāļĄāļđāļĨāļŠāđāļ§āļāļāļļāļāļāļĨāļāļĩāđāļĄāļĩāļāļ§āļēāļĄāļāđāļāļāđāļŦāļ§ (āļāđāļēāļĄāļĩ) āļāļāļāļāļēāļāđāļĢāļāļđāđāļĄāđāđāļĨāļ°āđāļāļāļŠāļēāļĢāļāļ·āđāļāđāļāļāđāļāļāļāļĩāđāļāļ°āļāļąāļāđāļŦāļĨāļāđāļāļāļŠāļēāļĢāļāļąāļāļāļĨāđāļēāļ§āđāļ§āđāļāļāđāļ§āđāļāđāļāļāđāđāļĨāđāļ§āļāđāļ§āļĒ āļāļąāđāļāļāļĩāđ āļāļāļēāļāļēāļĢāļĄāļĩāļāļ§āļēāļĄāļāļģāđāļāđāļāļāđāļāļāđāļāđāļāļĢāļ§āļāļĢāļ§āļĄāļāđāļāļĄāļđāļĨāļŠāđāļ§āļāļāļļāļāļāļĨāđāļāļĩāđāļĒāļ§āļāļąāļāļāļĢāļ°āļ§āļąāļāļīāļāļēāļāļāļēāļāļĢāļĢāļĄāļāļāļāļāđāļēāļāđāļāļ·āđāļāļāļĢāļĢāļĨāļļāļ§āļąāļāļāļļāļāļĢāļ°āļŠāļāļāđāđāļāļāļēāļĢāļāļīāļāļēāļĢāļāļēāļĢāļąāļāļāļļāļāļāļĨāđāļāđāļēāļāļģāļāļēāļ āļŦāļĢāļ·āļāļāļēāļĢāļāļĢāļ§āļāļŠāļāļāļāļļāļāļŠāļĄāļāļąāļāļī āļĨāļąāļāļĐāļāļ°āļāđāļāļāļŦāđāļēāļĄ āļŦāļĢāļ·āļāļāļīāļāļēāļĢāļāļēāļāļ§āļēāļĄāđāļŦāļĄāļēāļ°āļŠāļĄāļāļāļāļāļļāļāļāļĨāļāļĩāđāļāļ°āđāļŦāđāļāļģāļĢāļāļāļģāđāļŦāļāđāļ āļāļķāđāļāļāļēāļĢāđāļŦāđāļāļ§āļēāļĄāļĒāļīāļāļĒāļāļĄāđāļāļ·āđāļāđāļāđāļāļĢāļ§āļāļĢāļ§āļĄ āđāļāđ āļŦāļĢāļ·āļāđāļāļīāļāđāļāļĒāļāđāļāļĄāļđāļĨāļŠāđāļ§āļāļāļļāļāļāļĨāđāļāļĩāđāļĒāļ§āļāļąāļāļāļĢāļ°āļ§āļąāļāļīāļāļēāļāļāļēāļāļĢāļĢāļĄāļāļāļāļāđāļēāļāļĄāļĩāļāļ§āļēāļĄāļāļģāđāļāđāļāļŠāļģāļŦāļĢāļąāļāļāļēāļĢāđāļāđāļēāļāļģāļŠāļąāļāļāļēāđāļĨāļ°āļāļēāļĢāđāļāđāļĢāļąāļāļāļēāļĢāļāļīāļāļēāļĢāļāļēāļāļēāļĄāļ§āļąāļāļāļļāļāļĢāļ°āļŠāļāļāđāļāļąāļāļāļĨāđāļēāļ§āļāđāļēāļāļāđāļ āđāļāļāļĢāļāļĩāļāļĩāđāļāđāļēāļāđāļĄāđāđāļŦāđāļāļ§āļēāļĄāļĒāļīāļāļĒāļāļĄāđāļāļāļēāļĢāđāļāđāļāļĢāļ§āļāļĢāļ§āļĄ āđāļāđ āļŦāļĢāļ·āļāđāļāļīāļāđāļāļĒāļāđāļāļĄāļđāļĨāļŠāđāļ§āļāļāļļāļāļāļĨāđāļāļĩāđāļĒāļ§āļāļąāļāļāļĢāļ°āļ§āļąāļāļīāļāļēāļāļāļēāļāļĢāļĢāļĄ āļŦāļĢāļ·āļāļĄāļĩāļāļēāļĢāļāļāļāļāļ§āļēāļĄāļĒāļīāļāļĒāļāļĄāđāļāļ āļēāļĒāļŦāļĨāļąāļ āļāļāļēāļāļēāļĢāļāļēāļāđāļĄāđāļŠāļēāļĄāļēāļĢāļāļāļģāđāļāļīāļāļāļēāļĢāđāļāļ·āđāļāļāļĢāļĢāļĨāļļāļ§āļąāļāļāļļāļāļĢāļ°āļŠāļāļāđāļāļąāļāļāļĨāđāļēāļ§āļāđāļēāļāļāđāļāđāļāđ āđāļĨāļ°āļāļēāļ āļāļģāđāļŦāđāļāđāļēāļāļŠāļđāļāđāļŠāļĩāļĒāđāļāļāļēāļŠāđāļāļāļēāļĢāđāļāđāļĢāļąāļāļāļēāļĢāļāļīāļāļēāļĢāļāļēāļĢāļąāļāđāļāđāļēāļāļģāļāļēāļāļāļąāļāļāļāļēāļāļēāļĢ .
āļāļąāļāļĐāļ°:
Data Analysis, Analytical Thinking, High Responsibilities
āļāļĢāļ°āđāļ āļāļāļēāļ:
āļāļēāļāļāļĢāļ°āļāļģ
āđāļāļīāļāđāļāļ·āļāļ:
āļŠāļēāļĄāļēāļĢāļāļāđāļāļĢāļāļāđāļāđ
- Analyze data and provide business insight.
- Assess risk and design mitigation actions based on data insight.
- Design dashboards / predictive models for decision-making management.
- Lead and Supervise team by projects.
āļāļąāļāļĐāļ°:
Industry trends, Statistics, Python
āļāļĢāļ°āđāļ āļāļāļēāļ:
āļāļēāļāļāļĢāļ°āļāļģ
āđāļāļīāļāđāļāļ·āļāļ:
āļŠāļēāļĄāļēāļĢāļāļāđāļāļĢāļāļāđāļāđ
- Develop and execute a forward-thinking analytics strategy tailored to the retail industry, focusing on leveraging data platforms to drive revenue growth, operational efficiency, and customer satisfaction.
- Lead, mentor, and inspire a team of data scientists and analysts, fostering a culture of innovation, collaboration, and data-driven decision-making.
- Stay ahead of industry trends, emerging technologies, and best practices in data science and retail analytics to maintain CP Axtra s competitive edge.
- Analytics Execution.
- Oversee the integration of diverse data sources, including POS systems, CRM platforms, online transactions, and third-party providers, into our cloud-based data platform.
- Design and develop advanced machine learning models, algorithms, and statistical analyses to uncover actionable insights related to customer behavior, product performance, and market trends.
- Apply expertise in recommendation and personalization algorithms to enhance customer experiences and engagement.
- Deliver data-driven solutions to optimize pricing strategies, inventory management, and promotional campaigns, leveraging state-of-the-art analytics tools and methodologies.
- Business Partnership.
- Partner closely with retail operations, marketing, and sales teams to understand business challenges and provide tailored analytical support that aligns with strategic objectives.
- Identify opportunities to enhance customer segmentation, personalized marketing efforts, and customer retention strategies through advanced data science techniques.
- Act as a key advisor to senior leadership, translating complex data insights into actionable recommendations and business value.
- Performance Monitoring and Optimization.
- Define and monitor key performance indicators (KPIs) related to retail operations, such as sales conversion rates, customer lifetime value, and basket analysis.
- Leverage analytics to continuously assess and optimize business processes, driving operational efficiency and profitability.
- Communication and Presentation.
- Present complex analytical findings, models, and recommendations to stakeholders in a clear, impactful, and visually compelling manner.
- Collaborate across departments to implement data-driven initiatives that align with CPaxtra s goals and drive tangible outcomes.
- Education and Experience.
- Bachelor s degree in Statistics, Mathematics, Computer Science, Data Science, Economics, or a related field (Master s or PhD strongly preferred).
- Extensive experience in analytics, data science, or business intelligence roles, with significant exposure to the retail industry.
- Technical Skills.
- Advanced proficiency in Python, R, SQL, and machine learning frameworks.
- Expertise in data visualization tools (e.g., Tableau, Power BI) and cloud-based data platforms (e.g., AWS, GCP, Azure).
- In-depth knowledge of big data technologies (e.g., Spark, Hadoop) and modern data engineering practices.
- Strong understanding of recommendation/personalization algorithms and data processing technologies.
- Leadership and Business Acumen.
- Proven ability to lead high-performing teams in a dynamic, fast-paced environment.
- Exceptional strategic thinking and problem-solving skills with a demonstrated focus on delivering business value.
- Deep understanding of retail operations, including inventory management, customer journey mapping, and merchandising strategies.
- 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°:
Big Data, Hive, SAS
āļāļĢāļ°āđāļ āļāļāļēāļ:
āļāļēāļāļāļĢāļ°āļāļģ
āđāļāļīāļāđāļāļ·āļāļ:
āļŠāļēāļĄāļēāļĢāļāļāđāļāļĢāļāļāđāļāđ
- Design, implement, and maintain data analytics pipelines and processing systems.
- Experience of data modelling techniques and integration patterns.
- Write data transformation jobs through code.
- Analyze large datasets to extract insights and identify trends.
- Perform data management through data quality tests, monitoring, cataloging, and governance.
- Knowledge in data infrastructure ecosystem.
- Collaborate with cross-functional teams to identify opportunities to leverage data to drive business outcomes.
- Build data visualizations to communicate findings to stakeholders.
- A willingness to learn and find solutions to complex problems.
- Stay up-to-date with the latest developments in data analytics and science.
- Experience migrating from on-premise data stores to cloud solutions.
- Knowledge of system design and platform thinking to build sustainable solution.
- Practical experience with modern and traditional Big Data stacks (e.g BigQuery, Spark, Databricks, duckDB, Impala, Hive, etc).
- Experience working with data warehouse solutions ELT solutions, tools, and techniques (e.g. Airflow, dbt, SAS, Matillion, Nifi).
- Experience with agile software delivery and CI/CD processes.
- Bachelor's or Master's degree in computer science, statistics, engineering, or a related field.
- At least 3 years of experience in data analysis and modeling.
- Proficiency in Python, and SQL.
- Experience with data visualization tools such as Tableau, Grafana or similar.
- Familiarity with cloud computing platforms, such as GCP, AWS or Databricks.
- Strong problem-solving skills and the ability to work independently as well as collaboratively.
- This role offers a clear path to advance into machine learning and AI with data quality and management, providing opportunities to work on innovative projects and develop new skills in these exciting fields..
- Contact: [email protected] (K.Thipwimon).
- āļāđāļēāļāļŠāļēāļĄāļēāļĢāļāļāđāļēāļāđāļĨāļ°āļĻāļķāļāļĐāļēāļāđāļĒāļāļēāļĒāļāļ§āļēāļĄāđāļāđāļāļŠāđāļ§āļāļāļąāļ§āļāļāļāļāļāļēāļāļēāļĢāļāļĢāļļāļāđāļāļĒ āļāļģāļāļąāļ (āļĄāļŦāļēāļāļ) āļāļĩāđ https://krungthai.com/th/content/privacy-policy āļāļąāđāļāļāļĩāđ āļāļāļēāļāļēāļĢāđāļĄāđāļĄāļĩāđāļāļāļāļēāļŦāļĢāļ·āļāļāļ§āļēāļĄāļāļģāđāļāđāļāđāļāđ āļāļĩāđāļāļ°āļāļĢāļ°āļĄāļ§āļĨāļāļĨāļāđāļāļĄāļđāļĨāļŠāđāļ§āļāļāļļāļāļāļĨāļāļĩāđāļĄāļĩāļāļ§āļēāļĄāļāđāļāļāđāļŦāļ§ āļĢāļ§āļĄāļāļķāļāļāđāļāļĄāļđāļĨāļāļĩāđāđāļāļĩāđāļĒāļ§āļāđāļāļāļĻāļēāļŠāļāļēāđāļĨāļ°/āļŦāļĢāļ·āļāļŦāļĄāļđāđāđāļĨāļŦāļīāļ āļāļķāđāļāļāļēāļāļāļĢāļēāļāļāļāļĒāļđāđāđāļāļŠāļģāđāļāļēāļāļąāļāļĢāļāļĢāļ°āļāļģāļāļąāļ§āļāļĢāļ°āļāļēāļāļāļāļāļāļāđāļēāļāđāļāđāļāļĒāđāļēāļāđāļ āļāļąāļāļāļąāđāļ āļāļĢāļļāļāļēāļāļĒāđāļēāļāļąāļāđāļŦāļĨāļāđāļāļāļŠāļēāļĢāđāļāđ āļĢāļ§āļĄāļāļķāļāļŠāļģāđāļāļēāļāļąāļāļĢāļāļĢāļ°āļāļģāļāļąāļ§āļāļĢāļ°āļāļēāļāļ āļŦāļĢāļ·āļāļāļĢāļāļāļāđāļāļĄāļđāļĨāļŠāđāļ§āļāļāļļāļāļāļĨāļāļĩāđāļĄāļĩāļāļ§āļēāļĄāļāđāļāļāđāļŦāļ§āļŦāļĢāļ·āļāļāđāļāļĄāļđāļĨāļāļ·āđāļāđāļ āļāļķāđāļāđāļĄāđāđāļāļĩāđāļĒāļ§āļāđāļāļāļŦāļĢāļ·āļāđāļĄāđāļāļģāđāļāđāļāļŠāļģāļŦāļĢāļąāļāļ§āļąāļāļāļļāļāļĢāļ°āļŠāļāļāđāđāļāļāļēāļĢāļŠāļĄāļąāļāļĢāļāļēāļāđāļ§āđāļāļāđāļ§āđāļāđāļāļāđ āļāļāļāļāļēāļāļāļĩāđ āļāļĢāļļāļāļēāļāļģāđāļāļīāļāļāļēāļĢāđāļŦāđāđāļāđāđāļāļ§āđāļēāđāļāđāļāļģāđāļāļīāļāļāļēāļĢāļĨāļāļāđāļāļĄāļđāļĨāļŠāđāļ§āļāļāļļāļāļāļĨāļāļĩāđāļĄāļĩāļāļ§āļēāļĄāļāđāļāļāđāļŦāļ§ (āļāđāļēāļĄāļĩ) āļāļāļāļāļēāļāđāļĢāļāļđāđāļĄāđāđāļĨāļ°āđāļāļāļŠāļēāļĢāļāļ·āđāļāđāļāļāđāļāļāļāļĩāđāļāļ°āļāļąāļāđāļŦāļĨāļāđāļāļāļŠāļēāļĢāļāļąāļāļāļĨāđāļēāļ§āđāļ§āđāļāļāđāļ§āđāļāđāļāļāđāđāļĨāđāļ§āļāđāļ§āļĒ āļāļąāđāļāļāļĩāđ āļāļāļēāļāļēāļĢāļĄāļĩāļāļ§āļēāļĄāļāļģāđāļāđāļāļāđāļāļāđāļāđāļāļĢāļ§āļāļĢāļ§āļĄāļāđāļāļĄāļđāļĨāļŠāđāļ§āļāļāļļāļāļāļĨāđāļāļĩāđāļĒāļ§āļāļąāļāļāļĢāļ°āļ§āļąāļāļīāļāļēāļāļāļēāļāļĢāļĢāļĄāļāļāļāļāđāļēāļāđāļāļ·āđāļāļāļĢāļĢāļĨāļļāļ§āļąāļāļāļļāļāļĢāļ°āļŠāļāļāđāđāļāļāļēāļĢāļāļīāļāļēāļĢāļāļēāļĢāļąāļāļāļļāļāļāļĨāđāļāđāļēāļāļģāļāļēāļ āļŦāļĢāļ·āļāļāļēāļĢāļāļĢāļ§āļāļŠāļāļāļāļļāļāļŠāļĄāļāļąāļāļī āļĨāļąāļāļĐāļāļ°āļāđāļāļāļŦāđāļēāļĄ āļŦāļĢāļ·āļāļāļīāļāļēāļĢāļāļēāļāļ§āļēāļĄāđāļŦāļĄāļēāļ°āļŠāļĄāļāļāļāļāļļāļāļāļĨāļāļĩāđāļāļ°āđāļŦāđāļāļģāļĢāļāļāļģāđāļŦāļāđāļ āļāļķāđāļāļāļēāļĢāđāļŦāđāļāļ§āļēāļĄāļĒāļīāļāļĒāļāļĄāđāļāļ·āđāļāđāļāđāļāļĢāļ§āļāļĢāļ§āļĄ āđāļāđ āļŦāļĢāļ·āļāđāļāļīāļāđāļāļĒāļāđāļāļĄāļđāļĨāļŠāđāļ§āļāļāļļāļāļāļĨāđāļāļĩāđāļĒāļ§āļāļąāļāļāļĢāļ°āļ§āļąāļāļīāļāļēāļāļāļēāļāļĢāļĢāļĄāļāļāļāļāđāļēāļāļĄāļĩāļāļ§āļēāļĄāļāļģāđāļāđāļāļŠāļģāļŦāļĢāļąāļāļāļēāļĢāđāļāđāļēāļāļģāļŠāļąāļāļāļēāđāļĨāļ°āļāļēāļĢāđāļāđāļĢāļąāļāļāļēāļĢāļāļīāļāļēāļĢāļāļēāļāļēāļĄāļ§āļąāļāļāļļāļāļĢāļ°āļŠāļāļāđāļāļąāļāļāļĨāđāļēāļ§āļāđāļēāļāļāđāļ āđāļāļāļĢāļāļĩāļāļĩāđāļāđāļēāļāđāļĄāđāđāļŦāđāļāļ§āļēāļĄāļĒāļīāļāļĒāļāļĄāđāļāļāļēāļĢāđāļāđāļāļĢāļ§āļāļĢāļ§āļĄ āđāļāđ āļŦāļĢāļ·āļāđāļāļīāļāđāļāļĒāļāđāļāļĄāļđāļĨāļŠāđāļ§āļāļāļļāļāļāļĨāđāļāļĩāđāļĒāļ§āļāļąāļāļāļĢāļ°āļ§āļąāļāļīāļāļēāļāļāļēāļāļĢāļĢāļĄ āļŦāļĢāļ·āļāļĄāļĩāļāļēāļĢāļāļāļāļāļ§āļēāļĄāļĒāļīāļāļĒāļāļĄāđāļāļ āļēāļĒāļŦāļĨāļąāļ āļāļāļēāļāļēāļĢāļāļēāļāđāļĄāđāļŠāļēāļĄāļēāļĢāļāļāļģāđāļāļīāļāļāļēāļĢāđāļāļ·āđāļāļāļĢāļĢāļĨāļļāļ§āļąāļāļāļļāļāļĢāļ°āļŠāļāļāđāļāļąāļāļāļĨāđāļēāļ§āļāđāļēāļāļāđāļāđāļāđ āđāļĨāļ°āļāļēāļ āļāļģāđāļŦāđāļāđāļēāļāļŠāļđāļāđāļŠāļĩāļĒāđāļāļāļēāļŠāđāļāļāļēāļĢāđāļāđāļĢāļąāļāļāļēāļĢāļāļīāļāļēāļĢāļāļēāļĢāļąāļāđāļāđāļēāļāļģāļāļēāļāļāļąāļāļāļāļēāļāļēāļĢ .
āļāļąāļāļĐāļ°:
Compliance, Data Analysis, Power BI
āļāļĢāļ°āđāļ āļāļāļēāļ:
āļāļēāļāļāļĢāļ°āļāļģ
āđāļāļīāļāđāļāļ·āļāļ:
āļŠāļēāļĄāļēāļĢāļāļāđāļāļĢāļāļāđāļāđ
- Build and maintain an HR data repository tailored to the food business under ThaiBev group, focusing on metrics critical to food operations, such as labor productivity, turnover by location, and shift coverage efficiency.
- Ensure data integrity and compliance with industry-specific labor regulations, maintaining a transparent and accurate source of HR information.
- Collaborate with operations teams to integrate labor data from multiple food business units, enabling holistic insights across various branches and regions.
- Assist HR Line Manager on Strategic HR Analytics for Workforce OptimizationConduct data analysis on staffing patterns, turnover rates, and workforce efficiency to identify optimization opportunities aligned with food business cycles.
- Use predictive analytics to anticipate workforce needs for peak and off-peak seasons, aiding in proactive staffing and cost control with operation team to centralization.
- Assist on Commercial Structure and Labor Cost Management for Food OperationsAnalyze labor costs relative to revenue and operational efficiency within different food outlets, providing insights to optimize staffing while maximizing profitability.
- Support the development of labor cost budgets that align with product pricing and sales targets in the food sector, helping maintain competitive yet profitable operations.
- Generate regular reports on labor cost performance against targets, identifying areas for improvement and enabling business leaders to adjust strategy as needed.
- Be Leader on developing Power BI Development for Real-Time Food Business InsightsDesign and deploy Power BI dashboards specific to food operations, offering real-time insights on key metrics like labor costs, staffing levels, and turnover rates across outlets.
- Collaborate with senior leaders in the food division to customize dashboards, highlighting KPIs that impact food production, service speed, and customer satisfaction.
- Continuously update Power BI capabilities to provide comprehensive, up-to-date views on HR metrics essential to food business strategy.
- 3+ years of experience in analytics, data management not specific in HR experience.
- Demonstrated proficiency in Power BI development and advanced Excel skills, including VBA, macros, and pivot tables.
- Prior experience in labor cost analysis, commercial structure evaluation.
- Contact Information:-.
- Oishi Group Public Company Limited.
- CW Tower, No.90. Ratchadapisek Road, Huai Khwang, Bangkok.
āļāļąāļāļĐāļ°:
Excel
āļāļĢāļ°āđāļ āļāļāļēāļ:
āļāļēāļāļāļĢāļ°āļāļģ
āđāļāļīāļāđāļāļ·āļāļ:
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āļāļąāļāļĐāļ°:
Research, Statistics, Python, English
āļāļĢāļ°āđāļ āļāļāļēāļ:
āļāļēāļāļāļĢāļ°āļāļģ
āđāļāļīāļāđāļāļ·āļāļ:
āļŠāļēāļĄāļēāļĢāļāļāđāļāļĢāļāļāđāļāđ
- Apply statistical and machine learning methods to large, complex data sets to draw insights and provide actionable recommendations.
- Solve complex problems on both technical and business sides using advanced analytical methods.
- Work with Engineering teams to implement end-to-end process from model development to testing, validation, and deployment.
- Research and develop new quantitative models and frameworks to enhance the company s data science capability.
- Basic Qualifications Bachelor s degree in Engineering, Computer Science, Math, Physics, Statistics or other areas that are highly quantitative.
- Experience with statistical programming languages (e.g., Python, R, pandas) and database software (e.g., SQL, PySpark).
- Knowledge in statistics (e.g., hypothesis testing, regression) and machine learning.
- Strong analytical problem-solving capabilities.
- Preferred Qualifications Master s or PhD degree in a quantitative discipline.
- Experience applying machine learning and statistical methods to large datasets.
- Solid understanding of advanced statistics and machine learning practices.
- Experience in one or more specialized machine learning areas (e.g., NLP, deep learning, recommendation systems, reinforcement learning).
- Outstanding coding skills or software development background.
- Ability to think independently and communicate complex ideas to less technical persons.
- Excellent command of English in both verbal and written forms.
- We're committed to bringing passion and customer focus to the business. If you like wild growth and working with happy, enthusiastic over-achievers, you'll enjoy your career with us.
āļāļąāļāļĐāļ°:
Big Data, Research, Statistics
āļāļĢāļ°āđāļ āļāļāļēāļ:
āļāļēāļāļāļĢāļ°āļāļģ
āđāļāļīāļāđāļāļ·āļāļ:
āļŠāļēāļĄāļēāļĢāļāļāđāļāļĢāļāļāđāļāđ
- Design, code, experiment and implement models and algorithms to maximize customer experience, supply side value, business outcomes, and infrastructure readiness.
- Mine a big data of hundreds of millions of customers and more than 600M daily user generated events, supplier and pricing data, and discover actionable insights to drive improvements and innovation.
- Work with developers and a variety of business owners to deliver daily results with the best quality.
- Research discover and harness new ideas that can make a difference.
- What You'll Need to Succeed.
- 4+ years hands-on data science experience.
- Excellent understanding of AI/ML/DL and Statistics, as well as coding proficiency using related open source libraries and frameworks.
- Significant proficiency in SQL and languages like Python, PySpark and/or Scala.
- Can lead, work independently as well as play a key role in a team.
- Good communication and interpersonal skills for working in a multicultural work environment.
- It's Great if You Have.
- PhD or MSc in Computer Science / Operations Research / Statistics or other quantitative fields.
- Experience in NLP, image processing and/or recommendation systems.
- Hands on experience in data engineering, working with big data framework like Spark/Hadoop.
- Experience in data science for e-commerce and/or OTA.
- We welcome both local and international applications for this role. Full visa sponsorship and relocation assistance available for eligible candidates.
- Equal Opportunity Employer.
- At Agoda, we pride ourselves on being a company represented by people of all different backgrounds and orientations. We prioritize attracting diverse talent and cultivating an inclusive environment that encourages collaboration and innovation. Employment at Agoda is based solely on a person's merit and qualifications. We are committed to providing equal employment opportunity regardless of sex, age, race, color, national origin, religion, marital status, pregnancy, sexual orientation, gender identity, disability, citizenship, veteran or military status, and other legally protected characteristics.
- We will keep your application on file so that we can consider you for future vacancies and you can always ask to have your details removed from the file. For more details please read our privacy policy.
- To all recruitment agencies: Agoda does not accept third party resumes. Please do not send resumes to our jobs alias, Agoda employees or any other organization location. Agoda is not responsible for any fees related to unsolicited resumes.
āļāļąāļāļĐāļ°:
SQL, Oracle, Data Warehousing
āļāļĢāļ°āđāļ āļāļāļēāļ:
āļāļēāļāļāļĢāļ°āļāļģ
āđāļāļīāļāđāļāļ·āļāļ:
āļŠāļēāļĄāļēāļĢāļāļāđāļāļĢāļāļāđāļāđ
- Bachelor s degree in Computer Science, Information Systems, Engineering, or a related field.
- At least 7 years of experience as a Data Engineer or in a related role.
- Hands-on experience with SQL, database management (e.g., Oracle, SQL Server, PostgreSQL), and data warehousing concepts.
- Experience with ETL/ELT tools such as Talend, Apache NiFi, or similar.
- Proficiency in programming languages like Python, Java, or Scala for data manipulation and automation.
- Experience with cloud platforms such as AWS, Azure, or GCP.
- Knowledge of big data technologies such as Hadoop, Spark, or Kafka.
- Strong understanding of data governance, security, and privacy frameworks in a financial services context.
- Excellent problem-solving skills and attention to detail.
- Experience working with Data Visualization or BI tools like Power BI, Tableau.
- Familiarity with machine learning concepts, model deployment, and AI applications.
- Banking or financial services industry experience, especially in retail or wholesale banking data solutions.
- Certification in cloud platforms (e.g., AWS Certified Data Engineer, Microsoft Azure Data Engineer, Google Professional Data Engineer)..
- Contact:.
- 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°:
Automation
āļāļĢāļ°āđāļ āļāļāļēāļ:
āļāļēāļāļāļĢāļ°āļāļģ
āđāļāļīāļāđāļāļ·āļāļ:
āļŠāļēāļĄāļēāļĢāļāļāđāļāļĢāļāļāđāļāđ
- Lead cross-functional projects using data modeling and analysis techniques to discover insights that will guide strategic decisions and uncover optimization opportunities.
- Build, develop and maintain data models, reporting systems, data automation systems, dashboards and performance metrics to support key business decisions.
- Examine, interpret and report results of analytical initiatives to management, product, marketing and sales team.
- We're committed to bringing passion and customer focus to the business. If you like wild growth and working with happy, enthusiastic over-achievers, you'll enjoy your career with us.
āļāļąāļāļĐāļ°:
Assurance, Compliance
āļāļĢāļ°āđāļ āļāļāļēāļ:
āļāļēāļāļāļĢāļ°āļāļģ
āđāļāļīāļāđāļāļ·āļāļ:
āļŠāļēāļĄāļēāļĢāļāļāđāļāļĢāļāļāđāļāđ
- Conduct detailed analysis of Enterprise Service revenue to identify trends in products and services within AIS Group.
- Verify the accuracy and completeness of revenue collection, promotion packages, and new services to ensure compliance with business conditions.
- Develop appropriate QA measures to minimize revenue loss and operational errors.
- Detect and investigate irregularities affecting revenue, such as real loss, opportunity loss, and fraud.
- Collaborate with relevant departments to address and rectify issues impacting revenue.
- Ensure the accuracy of service charges, promotion packages, and offerings for enterprise customers.
- Review and validate the calculation of postpaid voice, IDD, and IR services in the RBM system to prevent revenue loss.
- Utilize data analytics skills to analyze data from various sources, reflecting trends, performance, and efficiency of products and services.
- Prepare analysis reports to support management in strategy formulation and risk assessment.
āļāļąāļāļĐāļ°:
Project Management, Scrum, Product Owner
āļāļĢāļ°āđāļ āļāļāļēāļ:
āļāļēāļāļāļĢāļ°āļāļģ
āđāļāļīāļāđāļāļ·āļāļ:
āļŠāļēāļĄāļēāļĢāļāļāđāļāļĢāļāļāđāļāđ
- Bachelor s degree in Business Administration, Innovation Management, Computer Science, Data Science, or a related field.
- At least 7 years of experience managing projects, with at least 2 years focused on innovation, digital transformation, or emerging technologies.
- Proven experience in end-to-end project management, including planning, execution, monitoring, and delivery.
- Experience working with project management tools such as JIRA, Trello, Asana, or MS Project.
- Knowledge of data analytics frameworks, predictive models, and data-driven decision-making methodologies.
- Understanding of emerging technologies (e.g., AI, machine learning, Generative AI, IoT) and innovation frameworks.
- Strong analytical, problem-solving, and decision-making skills.
- Excellent stakeholder management, communication, and presentation skills.
- Ability to work in a fast-paced, agile environment with cross-functional teams.
- Certifications: PMP, Prince2, Agile (Scrum Master, Product Owner), or Design Thinking certifications.
- Contact:.
- āļāđāļēāļāļŠāļēāļĄāļēāļĢāļāļāđāļēāļāđāļĨāļ°āļĻāļķāļāļĐāļēāļāđāļĒāļāļēāļĒāļāļ§āļēāļĄāđāļāđāļāļŠāđāļ§āļāļāļąāļ§āļāļāļāļāļāļēāļāļēāļĢāļāļĢāļļāļāđāļāļĒ āļāļģāļāļąāļ (āļĄāļŦāļēāļāļ) āļāļĩāđ https://krungthai.com/th/content/privacy-policy āļāļąāđāļāļāļĩāđ āļāļāļēāļāļēāļĢāđāļĄāđāļĄāļĩāđāļāļāļāļēāļŦāļĢāļ·āļāļāļ§āļēāļĄāļāļģāđāļāđāļāđāļāđ āļāļĩāđāļāļ°āļāļĢāļ°āļĄāļ§āļĨāļāļĨāļāđāļāļĄāļđāļĨāļŠāđāļ§āļāļāļļāļāļāļĨāļāļĩāđāļĄāļĩāļāļ§āļēāļĄāļāđāļāļāđāļŦāļ§ āļĢāļ§āļĄāļāļķāļāļāđāļāļĄāļđāļĨāļāļĩāđāđāļāļĩāđāļĒāļ§āļāđāļāļāļĻāļēāļŠāļāļēāđāļĨāļ°/āļŦāļĢāļ·āļāļŦāļĄāļđāđāđāļĨāļŦāļīāļ āļāļķāđāļāļāļēāļāļāļĢāļēāļāļāļāļĒāļđāđāđāļāļŠāļģāđāļāļēāļāļąāļāļĢāļāļĢāļ°āļāļģāļāļąāļ§āļāļĢāļ°āļāļēāļāļāļāļāļāļāđāļēāļāđāļāđāļāļĒāđāļēāļāđāļ āļāļąāļāļāļąāđāļ āļāļĢāļļāļāļēāļāļĒāđāļēāļāļąāļāđāļŦāļĨāļāđāļāļāļŠāļēāļĢāđāļāđ āļĢāļ§āļĄāļāļķāļāļŠāļģāđāļāļēāļāļąāļāļĢāļāļĢāļ°āļāļģāļāļąāļ§āļāļĢāļ°āļāļēāļāļ āļŦāļĢāļ·āļāļāļĢāļāļāļāđāļāļĄāļđāļĨāļŠāđāļ§āļāļāļļāļāļāļĨāļāļĩāđāļĄāļĩāļāļ§āļēāļĄāļāđāļāļāđāļŦāļ§āļŦāļĢāļ·āļāļāđāļāļĄāļđāļĨāļāļ·āđāļāđāļ āļāļķāđāļāđāļĄāđāđāļāļĩāđāļĒāļ§āļāđāļāļāļŦāļĢāļ·āļāđāļĄāđāļāļģāđāļāđāļāļŠāļģāļŦāļĢāļąāļāļ§āļąāļāļāļļāļāļĢāļ°āļŠāļāļāđāđāļāļāļēāļĢāļŠāļĄāļąāļāļĢāļāļēāļāđāļ§āđāļāļāđāļ§āđāļāđāļāļāđ āļāļāļāļāļēāļāļāļĩāđ āļāļĢāļļāļāļēāļāļģāđāļāļīāļāļāļēāļĢāđāļŦāđāđāļāđāđāļāļ§āđāļēāđāļāđāļāļģāđāļāļīāļāļāļēāļĢāļĨāļāļāđāļāļĄāļđāļĨāļŠāđāļ§āļāļāļļāļāļāļĨāļāļĩāđāļĄāļĩāļāļ§āļēāļĄāļāđāļāļāđāļŦāļ§ (āļāđāļēāļĄāļĩ) āļāļāļāļāļēāļāđāļĢāļāļđāđāļĄāđāđāļĨāļ°āđāļāļāļŠāļēāļĢāļāļ·āđāļāđāļāļāđāļāļāļāļĩāđāļāļ°āļāļąāļāđāļŦāļĨāļāđāļāļāļŠāļēāļĢāļāļąāļāļāļĨāđāļēāļ§āđāļ§āđāļāļāđāļ§āđāļāđāļāļāđāđāļĨāđāļ§āļāđāļ§āļĒ āļāļąāđāļāļāļĩāđ āļāļāļēāļāļēāļĢāļĄāļĩāļāļ§āļēāļĄāļāļģāđāļāđāļāļāđāļāļāđāļāđāļāļĢāļ§āļāļĢāļ§āļĄāļāđāļāļĄāļđāļĨāļŠāđāļ§āļāļāļļāļāļāļĨāđāļāļĩāđāļĒāļ§āļāļąāļāļāļĢāļ°āļ§āļąāļāļīāļāļēāļāļāļēāļāļĢāļĢāļĄāļāļāļāļāđāļēāļāđāļāļ·āđāļāļāļĢāļĢāļĨāļļāļ§āļąāļāļāļļāļāļĢāļ°āļŠāļāļāđāđāļāļāļēāļĢāļāļīāļāļēāļĢāļāļēāļĢāļąāļāļāļļāļāļāļĨāđāļāđāļēāļāļģāļāļēāļ āļŦāļĢāļ·āļāļāļēāļĢāļāļĢāļ§āļāļŠāļāļāļāļļāļāļŠāļĄāļāļąāļāļī āļĨāļąāļāļĐāļāļ°āļāđāļāļāļŦāđāļēāļĄ āļŦāļĢāļ·āļāļāļīāļāļēāļĢāļāļēāļāļ§āļēāļĄāđāļŦāļĄāļēāļ°āļŠāļĄāļāļāļāļāļļāļāļāļĨāļāļĩāđāļāļ°āđāļŦāđāļāļģāļĢāļāļāļģāđāļŦāļāđāļ āļāļķāđāļāļāļēāļĢāđāļŦāđāļāļ§āļēāļĄāļĒāļīāļāļĒāļāļĄāđāļāļ·āđāļāđāļāđāļāļĢāļ§āļāļĢāļ§āļĄ āđāļāđ āļŦāļĢāļ·āļāđāļāļīāļāđāļāļĒāļāđāļāļĄāļđāļĨāļŠāđāļ§āļāļāļļāļāļāļĨāđāļāļĩāđāļĒāļ§āļāļąāļāļāļĢāļ°āļ§āļąāļāļīāļāļēāļāļāļēāļāļĢāļĢāļĄāļāļāļāļāđāļēāļāļĄāļĩāļāļ§āļēāļĄāļāļģāđāļāđāļāļŠāļģāļŦāļĢāļąāļāļāļēāļĢāđāļāđāļēāļāļģāļŠāļąāļāļāļēāđāļĨāļ°āļāļēāļĢāđāļāđāļĢāļąāļāļāļēāļĢāļāļīāļāļēāļĢāļāļēāļāļēāļĄāļ§āļąāļāļāļļāļāļĢāļ°āļŠāļāļāđāļāļąāļāļāļĨāđāļēāļ§āļāđāļēāļāļāđāļ āđāļāļāļĢāļāļĩāļāļĩāđāļāđāļēāļāđāļĄāđāđāļŦāđāļāļ§āļēāļĄāļĒāļīāļāļĒāļāļĄāđāļāļāļēāļĢāđāļāđāļāļĢāļ§āļāļĢāļ§āļĄ āđāļāđ āļŦāļĢāļ·āļāđāļāļīāļāđāļāļĒāļāđāļāļĄāļđāļĨāļŠāđāļ§āļāļāļļāļāļāļĨāđāļāļĩāđāļĒāļ§āļāļąāļāļāļĢāļ°āļ§āļąāļāļīāļāļēāļāļāļēāļāļĢāļĢāļĄ āļŦāļĢāļ·āļāļĄāļĩāļāļēāļĢāļāļāļāļāļ§āļēāļĄāļĒāļīāļāļĒāļāļĄāđāļāļ āļēāļĒāļŦāļĨāļąāļ āļāļāļēāļāļēāļĢāļāļēāļāđāļĄāđāļŠāļēāļĄāļēāļĢāļāļāļģāđāļāļīāļāļāļēāļĢāđāļāļ·āđāļāļāļĢāļĢāļĨāļļāļ§āļąāļāļāļļāļāļĢāļ°āļŠāļāļāđāļāļąāļāļāļĨāđāļēāļ§āļāđāļēāļāļāđāļāđāļāđ āđāļĨāļ°āļāļēāļ āļāļģāđāļŦāđāļāđāļēāļāļŠāļđāļāđāļŠāļĩāļĒāđāļāļāļēāļŠāđāļāļāļēāļĢāđāļāđāļĢāļąāļāļāļēāļĢāļāļīāļāļēāļĢāļāļēāļĢāļąāļāđāļāđāļēāļāļģāļāļēāļāļāļąāļāļāļāļēāļāļēāļĢ .
āļāļĢāļ°āļŠāļāļāļēāļĢāļāđ:
5 āļāļĩāļāļķāđāļāđāļ
āļāļąāļāļĐāļ°:
Statistics, Finance, Risk Management
āļāļĢāļ°āđāļ āļāļāļēāļ:
āļāļēāļāļāļĢāļ°āļāļģ
āđāļāļīāļāđāļāļ·āļāļ:
āļŠāļēāļĄāļēāļĢāļāļāđāļāļĢāļāļāđāļāđ
- Bachelor s degree (or equivalent) degree in a quantitative field such as Data Science, Actuarial Science, Statistics, or Mathematics.
- 5+ years of related practical experience, preferably in commercial insurance sector.
- Solid understanding of insurance pricing principles, loss reserving, and risk assessment methodologies.
- Familiarity with insurance industry regulations, standards, and best practices.
- Develop and maintain loss cost models using GLMs and other advanced statistical techniques, incorporating relevant variables and factors for accurate pricing and risk assessment.
- Analyse historical insurance data to identify patterns and trends, and determine the impact of various factors on loss costs.
- Collaborate with underwriting, claims, and finance teams to understand business needs and provide data-driven insights for portfolio management.
- Conduct rate level reviews to ensure appropriate pricing of insurance products, considering risk exposure, market dynamics, and profitability goals.
- Enhance loss cost models over time by incorporating new data sources, refining variables,.
- and exploring innovative modelling techniques.
- Evaluate the impact of pricing strategies, policy changes, and market shifts on portfolio performance, and make recommendations for adjustments, if needed.
- Present findings and recommendations to stakeholders, including senior management and underwriting teams, in clear and concise reports.
- Work closely with other departments including Underwriting, Actuarial, and Risk Management, providing them with the data and insights needed to make evidence-based decisions.
- Functional Competency.
- Excellent analytical and problem-solving skills, with the ability to translate data into meaningful insights and recommendations.
- Strong communication skills to effectively convey complex findings and recommendations to both technical and non-technical stakeholders.
- Attention to detail and ability to work independently, managing multiple projects and deadlines efficiently.
- Strong proficiency in statistical modeling techniques, specifically GLMs, and experience with software tools like R, SAS, or Python.
- Proficiency with data analysis and visualisation tools and platforms, preferably Qliksense, Power BI, Alteryx, etc.
- Educational.
- Bachelor s degree (or equivalent) degree in a quantitative field such as Data Science, Actuarial Science, Statistics, or Mathematics.
- 5+ years of related practical experience, preferably in commercial insurance sector.
- Solid understanding of insurance pricing principles, loss reserving, and risk assessment methodologies.
- Familiarity with insurance industry regulations, standards, and best practices.
- Develop and maintain loss cost models using GLMs and other advanced statistical techniques, incorporating relevant variables and factors for accurate pricing and risk assessment.
- Analyse historical insurance data to identify patterns and trends, and determine the impact of various factors on loss costs.
- Collaborate with underwriting, claims, and finance teams to understand business needs and provide data-driven insights for portfolio management.
- Conduct rate level reviews to ensure appropriate pricing of insurance products, considering risk exposure, market dynamics, and profitability goals.
- Enhance loss cost models over time by incorporating new data sources, refining variables,.
- and exploring innovative modelling techniques.
- Evaluate the impact of pricing strategies, policy changes, and market shifts on portfolio performance, and make recommendations for adjustments, if needed.
- Present findings and recommendations to stakeholders, including senior management and underwriting teams, in clear and concise reports.
- Work closely with other departments including Underwriting, Actuarial, and Risk Management, providing them with the data and insights needed to make evidence-based decisions.
- Functional Competency.
- Excellent analytical and problem-solving skills, with the ability to translate data into meaningful insights and recommendations.
- Strong communication skills to effectively convey complex findings and recommendations to both technical and non-technical stakeholders.
- Attention to detail and ability to work independently, managing multiple projects and deadlines efficiently.
- Strong proficiency in statistical modeling techniques, specifically GLMs, and experience with software tools like R, SAS, or Python.
- Proficiency with data analysis and visualisation tools and platforms, preferably Qliksense, Power BI, Alteryx, etc.
āļāļąāļāļĐāļ°:
SQL, Javascript, Research
āļāļĢāļ°āđāļ āļāļāļēāļ:
āļāļēāļāļāļĢāļ°āļāļģ
āđāļāļīāļāđāļāļ·āļāļ:
āļŋ45,000 - āļŋ55,000, āļŠāļēāļĄāļēāļĢāļāļāđāļāļĢāļāļāđāļāđ
- Manage and maintain large datasets related to affiliate operations, ensuring data accuracy and integrity.
- Develop, optimize, and execute SQL queries to extract, manipulate, and analyze data for reporting and strategic planning.
- Use JavaScript, HTML, and CSS to design, implement, and enhance data visualization tools and dashboards.
- Provide in-depth analysis of affiliate program performance, identifying trends and recommending actionable strategies.
- Collaborate with internal teams (key account, campaign marketing, commercial, product) to define metrics, set KPIs, and support data-driven decisions.
- Automate routine reporting tasks and build dynamic dashboards to streamline affiliate program monitoring.
- Research and resolve data discrepancies, ensuring reliable and consistent reporting across the organization.
- Monitor the performance of affiliate campaigns and provide recommendations to improve effectiveness.
- Stay updated with the latest tools and technologies for data analysis and reporting to implement best practices.
- 4-5 years of experience in a data analysis role, preferably within an e-commerce or affiliate marketing environment.
- Advanced proficiency in SQL for data extraction, manipulation, and analysis.
- Hands-on experience with JavaScript, HTML, and CSS for data visualization and dashboard creation.
- Strong understanding of data management, reporting, and analytics tools.
- Proven ability to analyze complex datasets and provide clear, actionable insights.
- Detail-oriented, with excellent problem-solving and critical-thinking skills.
- Effective communication skills to present findings and recommendations to non-technical stakeholders.
- Experience with data visualization platforms like Tableau, Power BI, or Google Data Studio.
- Ability to work independently and as part of a team in a fast-paced environment.
āļāļĢāļ°āļŠāļāļāļēāļĢāļāđ:
āđāļĄāđāļāļģāđāļāđāļāļāđāļāļāļĄāļĩāļāļĢāļ°āļŠāļāļāļēāļĢāļāđāļāļģāļāļēāļ
āļāļąāļāļĐāļ°:
Electrical Engineering, Mechanical Engineering, English
āļāļĢāļ°āđāļ āļāļāļēāļ:
āļāļēāļāļāļĢāļ°āļāļģ
- Serve as the main contact for network investigations.
- Monitor GSA internal networks and data hall environments.
- Interpret and address connectivity alerts.
- Lead incident management events and create event tickets.
- Perform configuration tasks and adhere to security policies.
- Research and summarize events, providing reports.
- Coordinate with carriers to resolve customer issues.
- Provide input for network management optimization.
- Troubleshoot and escalate issues as needed.
- Deliver timely and accurate end-to-end support.
- Document actions and provide peer coaching/training.
- Job Qualifications.
- Bachelor's degree in information technology, computer science or related field.
- Flexible schedule availability, including nights, weekends, and shift rotations.
- Strong focus on customer service solutions.
- Understanding of various network topologies.
- Excellent communication skills via direct contact, phone, email, and documentation/tracking incidents.
- Knowledge of OSI Model and troubleshooting techniques.
- Familiarity with industry cabling standards and datacenter infrastructure.
- Proficiency in interacting with computing systems.
- Ability to navigate and utilize ticketing systems effectively.
- Comfortable working in a fast-paced environment with professionalism and flexibility.
- Punctual, reliable, and able to manage deadlines effectively.
- Strong organizational skills.
- Familiar with Computer literate with an emphasis on Microsoft Office Suite.
- Experience with equipment terminal access applications (Ex.: CRT, Putty, SSH).
- Experience with network monitoring software applications.
- We welcome recent graduates and those starting out in their careers to apply for this engineer-level position.
- Leader position is reserved for candidates with direct experience only.
- Creativity, problem solving skills, negotiation and systematic thinking.
- Fluent in English both written and verbal (Minimum 500 TOEIC score).
- Goal-Oriented, Unity, Learning, Flexible.
āļāļĢāļ°āļŠāļāļāļēāļĢāļāđ:
3 āļāļĩāļāļķāđāļāđāļ
āļāļąāļāļĐāļ°:
Microsoft Azure, SQL, UNIX, Python, Hadoop
āļāļĢāļ°āđāļ āļāļāļēāļ:
āļāļēāļāļāļĢāļ°āļāļģ
āđāļāļīāļāđāļāļ·āļāļ:
āļŠāļēāļĄāļēāļĢāļāļāđāļāļĢāļāļāđāļāđ
- 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 Qualit ...
- 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.
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āļĒāļāļāļāļīāļĒāļĄ
āļĨāļāļāļāļģ 5 āļŠāļīāđāļāļāļĩāđāļŦāļĨāļąāļāđāļĨāļīāļāļāļēāļ āļāļĩāļ§āļīāļāļāļļāļāļāļ°āđāļāļĨāļĩāđāļĒāļāđāļāļāļĨāļāļāļāļēāļĨ
āļāļģāđāļāļ°āļāļģāļāđāļēāļāļāļēāļāļĩāļāļāļĢāļīāļĐāļąāļ 7 āđāļāļāļāļĩāđāļāļļāļāđāļĄāđāļāļ§āļĢāļāļģāļāļēāļāļāđāļ§āļĒ
āļāļģāđāļāļ°āļāļģāļāļēāļĢāļŦāļēāļāļēāļāđāļāļīāļāđāļāļĨāļŠāļļāļāļĒāļāļ 50 āļāļĢāļīāļĐāļąāļāļāļĩāđāļāļāļĢāļļāđāļāđāļŦāļĄāđāļāļĒāļēāļāļĢāđāļ§āļĄāļāļēāļāļāđāļ§āļĒāļĄāļēāļāļāļĩāđāļŠāļļāļ 2024
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