Principal Data Scientist - Causal Inference

An optimization platform for Consumer Packaged Goods (CPG) leaders, translating commercial, supply chain and pricing complexity into dynamic decision workflows is seeking a Principal Data Scientist. This role centers on predictive modeling, causal inference, time-series forecasting and optimization.

You’ll build the analytical engines powering our CPG decision workflows- turning messy retail data (POS, syndicated data, trade promotion inputs, inventory logs) into predictive insights, bridging historical reporting and forward-looking experimentation. They are advised by leading academic experts in causal inference and want someone excited to bridge academia and industry., * Build and scale ML models and optimization routines (demand forecasting, price elasticity, trade promotion optimization)

• Build statistical frameworks measuring incremental lift of business actions, isolating real revenue drivers from noise
• Partner with engineering to structure noisy retail/billing data into clean, analysis-ready datasets
• Design rigorous A/B and multivariate tests for new decision workflows and features
• Translate statistical outputs into clear recommendations for product managers and executives

• Master’s (PhD a plus) in Statistics, Data Science, Applied Math, Economics, or CS
• 3+ years commercial data science experience; or 1 year of experience + PhD
• Strong Python (Pandas, NumPy, Scikit-learn) and SQL
• Deep knowledge of time-series forecasting, regression, and ML methods
• Experience with cloud data warehouses (Snowflake, BigQuery, or similar)

Preferred (not a must):

• CPG, e-commerce, or retail supply chain experience

Keywords: Causal Inference, Experimentation, Python, SQL