Data Science Engineer

• Design and implement optimization solutions for complex planning scenarios.
• Translate business and operational requirements into mathematical models, constraints, and objective functions.
• Develop and maintain production-grade Python modules, services, and integrations.
• Validate, test, debug, and improve optimization and data-processing workflows.
• Profile and improve model correctness, performance, and solution quality.
• Collaborate with engineering, product, and business stakeholders to clarify requirements and refine solutions.
• Document algorithms, assumptions, and technical decisions in a clear and structured way.
• Continuously improve modeling approaches, methodologies, and engineering practices.

• 5+ years of commercial experience in applied Data Science, Machine Learning, or optimization-focused software engineering.
• Strong Python skills, including modern Python practices, type hints, packaging, and virtual environments.
• Hands-on experience with numerical and data analysis libraries such as NumPy and pandas.
• Experience with mathematical optimization or combinatorial optimization in production environments.
• Practical knowledge of optimization tools such as Google OR-Tools CP-SAT, or similar solvers like Gurobi, CPLEX, PuLP, or Pyomo.
• Experience designing and validating optimization or simulation models for scheduling, routing, capacity planning, or similar use cases.
• Solid understanding of statistics and demand modeling.
• Experience with asynchronous and service-oriented Python applications.
• Strong analytical and structured problem-solving skills.
• Ability to explain complex technical concepts to non-technical stakeholders.
• Good written and verbal English communication skills.

Nice to have:

• Background in Operations Research, Applied Mathematics, Industrial Engineering, or another quantitative field.
• Experience tuning large-scale combinatorial or constraint-based optimization models.
• Familiarity with demand forecasting or pricing-related models.