ML Research Scientist

ML Research Scientist
About the Role:
As an ML Research Scientist, you'll develop advanced machine learning models for demand forecasting and inventory optimization. You'll solve complex, multi-dimensional problems involving perishable goods, supply chain dynamics, and retail operations at scale. Your work will directly impact inventory decisions, reducing food waste and ensuring fresh products for customers. This is a highly experimental role where intellectual curiosity and methodological rigor are key.

Core Responsibilities:

• Design and implement state-of-the-art ML models for demand forecasting at store, chain, and warehouse levels, accounting for seasonality, promotions, weather, and complex interactions.
• Develop optimization algorithms for multi-supplier sourcing, case size optimization, recipe planning, and inventory allocation.
• Build predictive analytics models for stock prediction, shelf-life optimization, and spoilage prevention using real-time data.
• Lead independent research initiatives to push the boundaries of retail forecasting.
• Design and execute rigorous A/B testing frameworks to validate model improvements and measure real-world impact.
• Technical Environment:
• Work with modern ML infrastructure for data orchestration, distributed computing, and model development. Data spans millions of SKUs, hundreds of stores, and billions of transactions.

Success Metrics:
Primary measure is forecast accuracy improvements that reduce food waste and increase profitability for retail partners.

Requirements:

4+ years of applied ML research experience, preferably in forecasting, optimization, or operations research.
Deep expertise in time series forecasting, with hands-on experience in PyTorch, XGBoost, or similar frameworks.
Strong mathematical foundation in optimization, probability, and statistics.
Proven track record of taking research ideas from conception to production.

Highly Desirable:

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

What's Offered:

• Impactful research addressing climate change and food security.
• Significant autonomy to explore novel approaches and drive research.
• Competitive base salary and equity.
• Fully employer-paid healthcare (medical, dental, vision).
• Unlimited vacation days.
• Fully covered food expenses in the office (lunch/dinner).
• Equinosx Gym membership.
• Conference & research budget for attending top ML/OR conferences and continuing education.

Tech Stack:

• ML & Research: PyTorch, XGBoost, Scikit-learn, NumPy, SciPy
• Data & Orchestration: Dagster, Dask, Coiled, Pandas, BigQuery
• Backend: Python, FastAPI
• Cloud & Infrastructure: GCP, Postgres, Terraform, Docker
• Frontend: React, Next.js, TypeScript