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# Principal Data Scientist - Causal Inference

- **Company:** Analytic Recruiting Inc.
- **Location:** United States (Remote available)
- **Salary:** $180,000.0
- **Contract:** Permanent contract
- **Skills:** Python (Programming Language), NumPy, Standard Sql, SAP Supply Chain Management (SCM), SQL Databases, Trade Promotion Management, Pandas, Scikit Learn
- **Published:** September 19, 2026
- **Apply:** https://www.analyticrecruiting.com/job-details.aspx?title=Principal+Data+Scientist+-+Causal+Inference&keywords=Causal+Inference%2C+Experimentation%2C+Python%2C+SQL&jobid=25019

## About the Role

* 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

## 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., * 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


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