🛠 Tech Stack

💼 About This Role

You'll build, test, and maintain ML models for demand forecasting and analytics science at a multi-state cannabis retailer. Your work will directly power inventory positioning and operational decisions across retail and wholesale. This is a hands-on role on a small, high-visibility team where you'll also help evolve the feature engineering roadmap.

🎯 What You'll Do

• Build and refine demand forecasting models across daily/weekly/monthly horizons
• Engineer features for the Snowflake Feature Store from retail sales and external data
• Develop and test model candidates against the backtesting framework
• Design and execute analytical studies to answer business-user operational questions

📋 Requirements

• 2+ years hands-on experience in data science or ML engineering role
• Strong Python skills (pandas, scikit-learn, statsmodels)
• Strong SQL skills with complex joins and multi-grain aggregations
• Working experience with supervised/unsupervised ML methods (gradient boosting, time series, etc.)

✨ Nice to Have

• Experience with time series forecasting (ARIMA, Prophet, LightGBM/XGBoost)
• Familiarity with feature store concepts or structured feature pipelines
• Exposure to Snowflake, dbt, or cloud data warehouses

🎁 Benefits & Perks

• 💰 Competitive pay based on experience and location
• 🏆 Discretionary annual incentive program
• 🏢 Hybrid work with 1 day in-office every 2 weeks
• 📈 Growth into shaping technical direction

📨 Hiring Process

Estimated timeline: 2-4 weeks · AI estimate

1. 1Recruiter screen· 30 min
2. 2Technical interview· 60 min
3. 3Hiring manager interview· 45 min