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, multi-source retail data (POS, syndicated data, trade promotion inputs, inventory logs) into predictive insight, 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.Location: Fully remoteSalary: Up to 180k base + equityResponsibilitiesBuild 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 executivesRequirements:Master's (PhD a plus) in Statistics, Data Science, Applied Math, Economics, or CS3+ years commercial data science experience; or 1 year of experience + PhDStrong Python (Pandas, NumPy, Scikit-learn) and SQLDeep knowledge of time-series forecasting, regression, and ML methodsExperience with cloud data warehouses (Snowflake, BigQuery, or similar)Dashboard/visualization skills (Tableau, PowerBI, Streamlit)Entrepreneurial mindset; comfortable shipping MVPs and iterating quicklyPreferred (not a must): CPG, e-commerce, or retail supply chain experienceBonus: potential to grow into a Head of Product or CPO role