Data Scientist

We are looking for a Data Scientist Supply Chain Optimization with 2+ years of relevant experience in solving complex business problems using mathematical optimization, heuristics, and Python-based solution development.

The ideal candidate should have strong expertise in MILP, heuristic and meta-heuristic techniques, along with a solid understanding of supply chain planning and operational decision-making. This role involves designing practical and scalable optimization solutions for real-world enterprise use cases such as production scheduling, inventory optimization, supply planning, allocation, and logistics optimization.

The candidate should be comfortable translating business requirements into mathematical models, building robust Python-based solutions, and working closely with business, product, and engineering teams to deliver deployable decision science solutions.

Must Have

• 2+ years of relevant experience in Data Science, Operations Research, Optimization, or Decision Science
• Strong 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, etc.)
• Strong proficiency in Python for model development and solution building
• Experience with one or more optimization frameworks / solvers such as:
• Pyomo
• PuLP
• OR-Tools
• Gurobi / CPLEX (preferred)
• Strong understanding of Supply Chain domain, with experience in one or more of the following areas:
• Supply Planning
• Production Planning / Scheduling
• Inventory Optimization
• Allocation / Replenishment
• Transportation / Logistics Optimization
• Strong problem-solving and analytical thinking skills
• Ability to formulate business problems into:
• Decision variables
• Constraints
• Objective functions
• Scenario / what-if models
• Experience building clean, modular, and maintainable Python solutions suitable for production environments
• Basic understanding of:

1. Packaging and deployment of Python applications / services
2. Linux / command-line fundamentals
3. Docker / containers

• Ability to collaborate effectively with cross-functional teams including business, product, engineering, and data teams

Good to Have

• Experience in real-world supply chain optimization deployments in production or enterprise environments
• Exposure to large-scale combinatorial optimization and performance tuning of optimization models
• Experience with hybrid solution approaches combining exact optimization with heuristics / rule-based methods
• Familiarity with:

1. SQL
2. Pandas / NumPy
3. FastAPI / Flask for model serving
4. Git / version control

• Exposure to cloud environments such as AWS / Azure / GCP
• Understanding of simulation, scenario planning, or decision intelligence platforms
• Exposure to Agentic AI / Agentic Systems or AI-assisted decision support concepts, such as:
• Multi-agent workflows
• Tool orchestration
• RAG / context-aware AI systems
• LLM-assisted decision support
• Ability to explain optimization logic, assumptions, and outputs clearly to business stakeholders