Data Scientist

Role & responsibilities

• Develop and deploy machine learning models for retail analytics, marketing effectiveness, pricing, promotions, and assortment optimization.
• Retail Media Measurement: Measure campaign incrementality and effectiveness using A/B testing, matched-market testing, Multi-Touch Attribution (MTA), and Marketing Mix Modeling (MMM).
• Retail Pricing & Promotions: Build pricing and promotion effectiveness models to measure sales lift, price elasticity, cannibalization, halo effects, and campaign/offer ROI.
• Retail Assortment: Develop assortment optimization, demand transfer, product substitution, and cross-product demand models.
• Apply statistical modelling, causal inference, experimentation, and predictive analytics to measure incremental sales, revenue, and profitability.
• Build scalable ML pipelines and productionize models in collaboration with data engineering and business teams.
• Translate analytical insights into actionable recommendations for marketing, pricing, merchandising, and category management.

Preferred candidate profile

• 5+ years of experience in Data Science, Machine Learning, or Advanced Analytics, preferably in retail, e-commerce, CPG, or retail media.
• Strong proficiency in Python, SQL, statistics, and machine learning algorithms.
• Hands-on experience in model development, validation, deployment, and monitoring.
• Experience in incrementality measurement, A/B testing, matched-market testing, and exposure to MTA/MMM.
• Experience in pricing and promotion analytics, including lift, cannibalization, halo effects, and ROI.
• Experience in assortment optimization, demand transfer, or product substitution modelling.
• Strong understanding of causal inference, experimental design, and business impact measurement.