Data Scientist / ML Engineer

Job Description:

• Translate planning, operations and sales business questions into modelable problems with clear success metrics
• Build demand forecasting models, including for irregular and sparse demand
• Formulate and solve optimization problems with LP/MIP solvers
• Use simulation to test scenarios
• Deploy, version, monitor and retrain models in production in close collaboration with data engineering
• Build and maintain APIs and services using Python and FastAPI on AWS, Azure or GCP
• Follow security best practices
• Design, build and deploy AI-enhanced applications
• Take machine learning solutions through to production

Requirements:

• Solid classical machine learning and statistics knowledge: regression, trees and boosting, regularization, probability and prediction intervals
• Correct validation, including temporal backtesting without data leakage, and metrics tied to business goals
• Time series and demand forecasting experience
• Operations research experience, including formulating optimization problems and solving them with Pyomo, OR-Tools, PuLP or similar
• Familiarity with Monte Carlo and discrete-event simulation, such as SimPy
• Conceptual knowledge of inventory and supply chain: safety stock, service level, lead time and turnover
• Fluent Python for data, including pandas, numpy and scikit-learn
• Advanced SQL
• Production-grade backend experience
• Proven experience taking models to production, including deployment, monitoring, drift detection and retraining
• Hands-on experience with a major cloud platform
• Experience with API design and backend security basics
• MSc or PhD in Operations Research, Industrial Engineering, Statistics or another quantitative field is nice to have
• Senior-level experience

Benefits:

• People-first company culture
• Supportive, trusted and empowering work environment
• Team collaboration and mutual support
• Remote work