Data Analytics Senior Associate

Job Summary

The Senior Data Analyst acts as the business partner between the Data and AI department and assigned business units, and is accountable for the business outcomes of analytics and AI products within that domain, not only for their delivery. The role owns the full path from business problem to adopted decision: framing and prioritising demand, determining which requests should not be pursued, defining trusted business metrics, delivering decision-ready analysis, and tracking whether the work actually changes decisions. The role applies AI and LLM-based tools throughout the analytical workflow, and produces the documented data context that enables the organisation’s AI assistants and agents to answer business questions from the assigned domain reliably. Performance is measured by decision quality and adoption within the business units served, rather than by the volume of reports or dashboards produced.

หน้าที่ความรับผิดชอบ (Responsibilities)

Overall governance

1. Policy Compliance: Ensure all analytical activities comply with internal data management policies, PDPA and data privacy regulations, and the corporate data and AI governance frameworks.
2. Responsible Use of AI: Apply AI and LLM-based tools in line with the corporate AI governance framework (ISO/IEC 42001 AIMS), including use of approved tools only, human review of AI-generated output before release, and disclosure of AI use in deliverables.
3. Metric and Definition Stewardship: Act as steward of business metric definitions within the assigned domain, ensuring each metric has a single documented definition approved by the data owner and applied consistently across all reporting and AI products.
4. Documentation and Auditability: Maintain documentation of data sources, assumptions, metric logic, and AI-assisted steps so that any analysis can be independently reviewed, reproduced, and audited.

Functional-related accountabilities

1. Business Partnership: Serve as the primary Data and AI counterpart for assigned business units, participating in their planning and decision cycles from the outset rather than receiving requirements after the decision has already been framed.
2. Problem Framing: Translate ambiguous business situations into well-defined analytical questions, and challenge the stated request where the underlying decision calls for a different approach.
3. Demand Prioritisation: Assess incoming requests against business value and effort, and recommend which are pursued, deferred, redirected to self-service, or declined, in order to protect team capacity for high-impact work.
4. Decision Support and Analysis: Deliver analysis that leads to a specific recommendation, including quantified options, trade-offs, risks, and expected business impact.
5. Adoption Ownership: Monitor usage and business impact of delivered analytics and AI products after handover, take corrective action where adoption does not materialise, and retire products that are no longer used.
6. AI-Assisted Analysis: Use LLM and Generative AI tools to accelerate data exploration, hypothesis generation, code development, and synthesis, while validating all outputs against source data before they inform a decision.
7. AI Product Enablement: Define and document the metrics, business rules, and data context required for corporate AI assistants and agents to answer questions from the assigned domain accurately, and verify the quality of their responses.
8. AI Output Evaluation: Evaluate the accuracy and reliability of AI-generated analytical output, identify recurring failure patterns, and provide structured feedback to the AI team to improve model and product performance.
9. Business Case and Benefit Tracking: Build the business case for analytics and AI use cases within the assigned domain, and track realised benefits against the original estimate after deployment.
10. Data Requirement Specification: Specify data requirements to Data Engineers and validate delivered datasets against business meaning and expected behaviour, ensuring analysis-ready data without building or maintaining pipelines directly.

Other accountabilities

1. Stakeholder Communication: Present findings and recommendations to business unit heads and executives, adapting depth, framing, and language to the audience.
2. Data Literacy Enablement: Coach business users to answer routine questions independently through self-service and AI tools, reducing recurring dependency on the Data and AI team.
3. Mentorship: Guide junior analysts on problem framing, analytical rigour, stakeholder engagement, and effective and responsible use of AI tools.
4. Cross-team Collaboration: Work with Data Engineers, AI Engineers, and IT to ensure business analytical needs are reflected in the data platform and AI product roadmaps.
5. Continuous Improvement: Evaluate emerging AI and analytical methods, and pilot those with clear applicability to the assigned domain.
6. Additional Responsibilities: Take on ad-hoc assignments as directed by the Data and AI Deputy Director, demonstrating initiative, ownership, and sound business judgement.