1. 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. 

2. 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. 

3. 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. 

4. 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 

5. 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. 

6. 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.