Data Scientist / ML Engineer

This is a fully remote position, open to applicants in United States.

📋 Description

• Convert business inquiries related to planning, operations, and sales into modelable challenges with defined success metrics.

• Develop demand forecasting models, including those for irregular and sparse demand.

• Formulate and resolve optimization problems utilizing LP/MIP solvers.

• Employ simulation techniques to assess various scenarios.

• Collaborate closely with data engineering to deploy, version, monitor, and retrain models in a production environment.

• Create and sustain APIs and services using Python and FastAPI on platforms such as AWS, Azure, or GCP.

• Adhere to best practices regarding security.

• Design, create, and launch AI-enhanced applications.

• Implement machine learning solutions through to production.

⛳️ Requirements

• Strong foundational knowledge in classical machine learning and statistics, including regression, trees and boosting, regularization, probability, and prediction intervals.

• Accurate validation practices, including temporal backtesting without data leakage, and metrics aligned with business objectives.

• Experience in time series analysis and demand forecasting.

• Background in operations research, focusing on formulating and solving optimization problems using tools like Pyomo, OR-Tools, PuLP, or similar.

• Familiarity with Monte Carlo and discrete-event simulation methods, such as SimPy.

• Conceptual understanding of inventory and supply chain principles, including safety stock, service levels, lead times, and turnover.

• Proficient in Python for data manipulation, including libraries such as pandas, numpy, and scikit-learn.

• Advanced SQL skills.

• Experience with production-grade backend systems.

• Demonstrated success in bringing models to production, encompassing deployment, monitoring, drift detection, and retraining.

• Practical experience with a major cloud service provider.

• Knowledge of API design and fundamental backend security practices.

• An MSc or PhD in Operations Research, Industrial Engineering, Statistics, or another quantitative discipline is preferred.

• Senior-level experience is required.

🏝️ Benefits

• A culture that prioritizes people.

• A supportive, trusted, and empowering workplace environment.

• Emphasis on team collaboration and mutual assistance.

• Flexibility for remote work.