Data Scientist

JOB DESCRIPTION Data Science - Retail domain client Designation – Data Science - Retail domain client Experience – 3-8Years Location- Bangalore (Client location) Job Description: 1. Problem Solving & Insights a. Translate ambiguous business problems into structured analytical approaches. b. Conduct exploratory analysis, driver analyses, funnel analyses, segmentation, forecasting, and hypothesis testing. c. Build clear, actionable insights that drive revenue, reduce cost, or improve customer and operational KPIs. 2. Model Development & Decision Frameworks a. Build predictive, prescriptive and causal models (e.g., churn, CLTV, attribution, price elasticity, anomaly detection, uplift modelling). b. Develop measurement frameworks, scorecards, and reusable decision-support tools for divisions. 3. Reusable Asset & Framework Creation a. Build modular, scalable assets that can be adopted by multiple divisions with minimal customization. b. Standardize logic, definitions, taxonomies and measurement approaches within the expertise area. 4. Deployment & Adoption a. Work with divisional analytics teams (hub & spoke model) to ensure adoption of frameworks, insights and models. b. Support one division deeply while enabling self-serve adoption for others, as per CoE operating model. 5. Collaboration a. Partner with Product, Tech, Category, Supply Chain, Marketing and Finance teams to understand pain points and define analytics-driven interventions. b. Work closely with Data Engineering teams on data readiness and pipeline requirements. 6. Innovation & Experiments a. Apply advanced ML/AI methods such as embeddings, causal inference, MMM, recommendation insights, anomaly detection, driver trees. b. Support controlled experiments (A/B testing) and design best practice experiment frameworks. Must-Have: 1. Strong SQL, Python/R, and statistical modelling skills. 2. Ability to translate business context into analytical problems. 3. Expertise relevant to the specific track (e.g., Customer Intelligence, Marketing Sciences, Pricing, Product Analytics, etc.). 4. Knowledge of ML/AI techniques: regression, classification, segmentation, causal inference, uplift modelling, MMM, forecasting, NLP, embeddings. 5. Experience with BI/visualization tools (Tableau, PowerBI) for insights and storytelling. 6. Understanding large-scale data environments, data hygiene, and measurement design. Good to Have 1. Structured problem solving and first principles thinking. 2. Strong ownership, bias for impact, and clarity in communication. 3. Ability to work in a lean, fast-paced, ambiguous setup. 4. High stakeholder empathy; ability to navigate cross-functional teams. 5. Curiosity, continuous learning, and the drive for excellence.