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- Problem Solving & Insights
- Translate ambiguous business problems into structured analytical approaches.
- Conduct exploratory analysis, driver analyses, funnel analyses, segmentation, forecasting, and hypothesis testing.
- Build clear, actionable insights that drive revenue, reduce cost, or improve customer and operational KPIs.
- Model Development & Decision Frameworks
- Build predictive, prescriptive and causal models (e.g., churn, RFM, attribution, promotions ROI/ effectiveness, anomaly detection, uplift modelling).
- Develop measurement frameworks, scorecards, and reusable decision-support tools for divisions.
- Reusable Asset & Framework Creation
- Build modular, scalable assets that can be adopted by multiple divisions with minimal customization.
- Standardize logic, definitions, taxonomies and measurement approaches within the expertise area.
- Deployment & Adoption
- Work with divisional analytics teams (hub & spoke model) to ensure adoption of frameworks, insights and models.
- Support one division deeply while enabling adoption for others
- Collaboration
- Partner with Product, Tech, Category, Supply Chain, Marketing and Finance teams to understand pain points and define analytics-driven interventions.
- Work closely with Data Engineering teams on data readiness and pipeline requirements.
- Innovation & Experiments
- Apply advanced ML/AI methods such as embeddings, causal inference, MMM, recommendation insights, anomaly detection, driver trees.
- Support controlled experiments (A/B testing) and design best practice experiment frameworks.