Principal Data Scientist - Causal Inference
Posted 3 days ago
analytic recruitingDenver (CO)
SENIORITY
Lead
About the role
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 remote
Salary: Up to 180k base + equity
Responsibilities:
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
Requirements:
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, Num Py, Scikit-learn) and SQLDeep knowledge of time-series forecasting, regression, and ML methods Experience with cloud data warehouses (Snowflake, Big Query, or similar)Dashboard/visualization skills (Tableau, PowerBI, Streamlit)Entrepreneurial mindset; comfortable shipping MVPs and iterating quickly Preferred (not a must): CPG, e-commerce, or retail supply chain experience
Bonus: potential to grow into a Head of Product or CPO role
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