Job Description:
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.
Location: Fully remote
Salary: Up to 180k base + equity
Responsibilities:
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Build and scale ML models and optimization routines (demand forecasting, price elasticity, trade promotion optimization)
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Build statistical frameworks measuring incremental lift of business actions, isolating real revenue drivers from noise
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Partner with engineering to structure noisy retail/billing data into clean, analysis-ready datasets
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Design rigorous A/B and multivariate tests for new decision workflows and features
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Translate statistical outputs into clear recommendations for product managers and executives
Requirements:
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Master's (PhD a plus) in Statistics, Data Science, Applied Math, Economics, or CS
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3+ years commercial data science experience; or 1 year of experience + PhD
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Strong Python (Pandas, NumPy, Scikit-learn) and SQL
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Deep knowledge of time-series forecasting, regression, and ML methods
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Experience with cloud data warehouses (Snowflake, BigQuery, or similar)
Preferred (not a must):
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CPG, e-commerce, or retail supply chain experience
Keywords: Causal Inference, Experimentation, Python, SQL
Qualified candidates, please send your resume to Hazem Kamal, Hazem@analyticrecruiting.com | For more opportunities, please visit www.analyticrecruiting.com
