Data Scientist / ML Engineer
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Job Description
Senior ML Engineer building forecasting, optimization, and AI applications for Ambush, an engineering, design, strategy, and AI consultancy. Deploying secure Python services and production machine-learning systems.
Responsibilities:
- 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
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