6 months ago
Remote, PhilippinesSenior
Responsibilities
- Build and improve inventory demand forecasting models using machine learning and statistical methods.
- Own machine learning models end-to-end, including data collection, feature engineering, training, deployment, monitoring, and iteration.
- Develop decision systems for inventory planning, pricing, and demand decisions.
- Build and maintain data pipelines and API integrations for internal and external data sources.
- Validate models rigorously using techniques such as leave-one-out validation, backtesting, and production-versus-offline metric comparisons.
- Implement LLM and AI-agent workflows, including prompt engineering and evaluation frameworks.
- Work independently in a small team, set priorities, resolve blockers, and communicate tradeoffs.
Requirements
- At least 5 years of Python experience building production ML systems beyond notebooks or research.
- Deep experience with statistical modeling, ensemble methods, k-nearest neighbors, calibration, cross-validation, and feature engineering.
- Expertise in time-series modeling and forecasting, including seasonality, trend decomposition, safety stock, and demand planning.
- A proven record of shipping machine learning models that drive business decisions in areas such as forecasting, pricing, or demand planning.
- Experience handling messy real-world data, including bias correction, stale signals, error cancellation, and distribution shifts.
- Experience with API integration and data pipeline architecture at scale.
- Hands-on experience with LLM and AI-agent workflows, prompt engineering, and evaluation frameworks.
- Experience with rigorous model validation, including leave-one-out validation, backtesting, and production-versus-offline metric gaps.
- Experience with Amazon marketplace, e-commerce, or retail analytics is preferred.
- Familiarity with similarity-based methods such as k-nearest neighbors, embeddings, and vector search is preferred.
- Experience maintaining long-lived model systems through many iteration cycles and prior startup or founder-adjacent experience are preferred.
Benefits
- Remote work from anywhere with a global team.
- Work-life balance, growth opportunities, and the opportunity to shape the role in an innovative, rapidly evolving environment.
- Full-time schedule of 9 a.m. to 6 p.m. Eastern Time.
Tech Stack
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