About the Role

Develop cutting-edge ML models for demand forecasting and inventory optimization to reduce food waste in grocery retail. Tackle complex problems involving perishable goods and supply chain dynamics at scale. Drive measurable business outcomes through research and experimentation.

Key Responsibilities

• Design and implement state-of-the-art ML models for demand forecasting
• Develop optimization algorithms for multi-supplier sourcing and inventory allocation
• Build models for stock prediction, shelf-life optimization, and spoilage prevention
• Lead independent research initiatives to advance retail forecasting
• Design and execute A/B testing frameworks to validate model improvements

Required Skills & Qualifications

Must Have:

• 4+ years of experience in applied machine learning research
• Deep expertise in time series forecasting with PyTorch or XGBoost
• Strong mathematical foundation in optimization, probability, and statistics
• Proven track record of taking research ideas from conception to production deployment

Nice to Have:

• Experience with inventory optimization, supply chain analytics, or retail forecasting
• Background in operations research, particularly multi-stage optimization and stochastic programming
• PhD in a quantitative field or equivalent industry experience
• Experience with distributed computing frameworks (Dask, PySpark, etc.)
• Publication record in top-tier ML/OR conferences or journals
• Experience working in high-growth startup environments

Benefits & Perks

• $180k-$220k base + competitive equity
• Fully employer-paid healthcare (medical, dental, and vision)
• Unlimited vacation days
• Fully covered food expenses in the office
• Free Equinox membership
• Conference & research budget