Analyze BD performance across the funnel (e.g., acquisition > activation > performance), merchant investment, and key productivity indicators; translate findings into actionable recommendations.
Perform rapid KPI spot-checks when metrics move unexpectedly: identify what changed, isolate where it changed, assess likely drivers, and recommend next steps.
Conduct deep-dive analyses using hypothesis-driven approaches and structured root-cause logic (MECE frameworks) to uncover growth levers and performance gaps.
Build clear narratives that connect analysis to decisions (trade-offs, impact size, confidence level, and recommended actions).
KPI Framework & Planning Support
Maintain and improve KPI frameworks: metric definitions, calculation logic, reporting cadence, and a single source of truth.
Support periodic planning cycles (half-year / annual) by building bottom-up projections: gather assumptions and action plans from stakeholders, translate them into topline forecasts, scenario models, and risk/opportunity assessments.
Partner with BD leaders to set targets grounded in data, historical trends, and market context; ensure assumptions are explicit and measurable.
Monitor forecast accuracy and refine drivers/assumptions over time.
Data Quality, Logic Validation & Governance
Validate data integrity end-to-end from raw sources to final outputs through sanity checks, reconciliation, outlier detection, and metric logic validation.
Proactively debug discrepancies and collaborate with BI, Product, and Ops teams to resolve tracking or pipeline issues.
Document metric definitions, assumptions, known limitations, and any changes to calculation logic to ensure transparency and repeatability.
Establish simple guardrails to prevent common analytics errors (e.g., duplicate joins, wrong partitions, misaligned time zones).
Dashboards, Reporting & Automation
Build and maintain dashboards and recurring reports that are accurate, scalable, and designed for self-serve consumption.
Drive efficiency by automating repetitive workflows (alerts, exports, validation routines) where possible.
Contribute to analytics infrastructure improvements: templates, playbooks, metric QA checklists, and standardized reporting practices.
Partner with BD, Marketing, Product, Ops, and Data teams to define success metrics and measurement plans for key growth initiatives.
Evaluate initiative impact using practical measurement approaches (e.g., before-after, segmentation, holdout logic where feasible) and communicate results with clarity.
Own analytics workstreams end-to-end from problem framing and stakeholder alignment to delivery and follow-through (with broader ownership expected at Senior Associate level).
Requirements
Requirement: Must-Have
Bachelor's degree or higher in Business, Economics, Analytics, Engineering, Statistics, or a related quantitative field.
Does not trust the number at face value, able to validate raw data, trace calculation logic, and challenge assumptions before concluding.
Strong MECE thinking with the ability to plan and execute quickly under ambiguity; comfortable breaking open-ended problems into clear analytical steps.
Proficient with CTEs, multi-table joins, window functions, aggregation, and debugging complex query logic in large-scale data environments.
Advanced Excel / Google Sheets skills; strong modelling ability and comfort working with large datasets.
Clear and concise in both Thai and English (written and verbal); able to present findings to technical and non-technical audiences.
Requirement: Nice-to-Have
Data Visualization: Tableau, Power BI, Looker Studio (or similar).
Automation & Scripting: Python for analysis/automation; Google Apps Script is a strong plus.
Industry Experience: Marketplace, e-commerce, food delivery, or other fast-paced tech environments.
Experimentation Mindset: Familiar with hypothesis design, guardrail metrics, and measurement discipline in A/B or quasi-experimental settings.
Stakeholder Ownership (Senior-leaning): Experience independently driving cross-functional projects to completion.
