6 months ago
Remote, Brazil or San José, Costa RicaSenior
Responsibilities
- Build and improve inventory demand forecasting models using machine learning and statistical methods.
- Own ML models end to end, including data collection, feature engineering, training, deployment, monitoring, and iteration.
- Develop decision systems supporting inventory planning, pricing, and demand decisions.
- Build and maintain data pipelines and API integrations for internal and external data sources.
- Validate model reliability using rigorous testing, backtesting, and production-versus-offline metric analysis.
- Implement LLM and AI-agent workflows for automating domain logic.
- Operate independently in a small team while setting priorities, resolving blockers, and communicating tradeoffs.
Requirements
- At least 5 years of Python experience building production ML systems beyond notebooks or research.
- Deep experience with statistical modeling, including ensemble methods, kNN, 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 ML models that drive business decisions in areas such as forecasting, pricing, or demand planning.
- Strong ability to work with messy real-world data, including bias correction, stale signals, error cancellation, and distribution shifts.
- Experience with API integration and scalable data pipeline architecture.
- Hands-on experience with LLM and AI-agent workflows, prompt engineering, and evaluation frameworks.
- Experience rigorously validating models using methods such as leave-one-out validation, backtesting, and production-versus-offline metric comparisons.
- Ability to work independently in a fast-evolving, small-team environment.
- Preferred experience with Amazon marketplace, e-commerce, or retail analytics; similarity-based methods such as kNN, embeddings, or vector search; long-lived model systems; or startup and founder-adjacent environments.
Benefits
- Fully remote work from anywhere with a global team.
- Work hours are 9 a.m. to 6 p.m. Eastern Time.
- Growth opportunities to shape the role and develop within the company.
- Innovative, data-focused work environment.
Tech Stack
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