We are seeking a talented Data Scientist with a specialization in Supply Chain Optimization. The ideal candidate will possess over 2 years of demonstrable experience in addressing complex business challenges through the application of mathematical optimization, heuristic methodologies, and Python-based solution development.

A core requirement for this role is strong expertise in Mixed Integer Linear Programming (MILP), alongside proficiency in heuristic and meta-heuristic techniques. A solid grasp of supply chain planning processes and operational decision-making is essential. This position involves the design and implementation of practical, scalable optimization solutions for critical enterprise use cases, including production scheduling, inventory optimization, supply planning, allocation strategies, and logistics optimization.

The successful applicant will be adept at translating intricate business requirements into precise mathematical models, constructing robust Python solutions, and collaborating effectively with business, product, and engineering teams to deliver deployable decision science solutions.

Key Responsibilities and Requirements

Must Have

• A minimum of 2 years of relevant professional experience in Data Science, Operations Research, Optimization, or Decision Science.
• Advanced hands-on expertise in:
  • MILP (Mixed Integer Linear Programming)
  • Linear / Integer Programming
  • Heuristic algorithms
  • Meta-Heuristic techniques (e.g., Genetic Algorithms, Simulated Annealing, Tabu Search)
• High proficiency in Python for the development of optimization models and solutions.
• Demonstrated experience with at least one of the following optimization frameworks/solvers:
  • Pyomo
  • PuLP
  • OR-Tools
  • Gurobi / CPLEX (preferred)
• A deep understanding of the Supply Chain domain, with practical experience in one or more of the following areas:
  • Supply Planning
  • Production Planning / Scheduling
  • Inventory Optimization
  • Allocation / Replenishment
  • Transportation / Logistics Optimization
• Exceptional problem-solving and analytical thinking capabilities.
• Proven ability to translate business problems into:
  • Decision variables
  • Constraints
  • Objective functions
  • Scenario / what-if models
• Experience in building clean, modular, and maintainable Python solutions suitable for production environments.
• Foundational knowledge of:
  • Packaging and deployment of Python applications/services
  • Linux/command-line fundamentals
  • Docker / containers
• Excellent collaboration skills, with the ability to work effectively with cross-functional teams including business, product, engineering, and data teams.

Good to Have

• Experience with real-world supply chain optimization deployments in production or enterprise settings.
• Exposure to large-scale combinatorial optimization and performance tuning of optimization models.
• Experience with hybrid solution approaches that integrate exact optimization with heuristics or rule-based methods.
• Familiarity with:
  • SQL
  • Pandas / NumPy
  • FastAPI / Flask for model serving
  • Git / version control
• Exposure to cloud computing platforms such as AWS, Azure, or GCP.
• Understanding of simulation, scenario planning, or decision intelligence platforms.
• Exposure to Agentic AI / Agentic Systems or AI-assisted decision support concepts, including:
  • Multi-agent workflows
  • Tool orchestration
  • RAG / context-aware AI systems
  • LLM-assisted decision support
• The ability to clearly articulate optimization logic, underlying assumptions, and derived outputs to business stakeholders.