We are looking for a Data Scientist with deep expertise in optimization and applied operations research to help solve complex scheduling, resource allocation, and flow optimization problems across large-scale operational environments. The ideal candidate will combine mathematical modeling skills with core data science capabilities to design and implement solutions that are both analytically rigorous and practically impactful.
Key Responsibilities :
- Design and build optimization models to support decision-making in scheduling, batch movement, or network planning
- Translate real-world business rules and constraints into mathematical formulations
- Develop and test optimization algorithms using solvers such as Gurobi, CPLEX, or Google OR-Tools
- Work with historical and real-time data to validate and tune models
- Collaborate with engineering, product, and business teams to integrate models into production environments
- Present insights and trade-offs to both technical and non-technical stakeholders
Required Skills & Experience :
5+ years of experience in data science, with strong focus on optimization or operations researchProficient in Python, relevant libraries and tools :Optimization : Pyomo, PuLP, OR-Tools, cvxpyData Science : pandas, NumPy, scikit-learn, matplotlib / seabornTools : Gurobi : CPLEX, or Google OR-ToolsHands-on experience with linear programming, mixed integer programming, or constraint programmingAbility to structure, clean, and analyze complex datasetsStrong communication skills for explaining technical models to diverse audiencesPreferred (Nice to Have) :
Experience working on scheduling, routing, or supply chain optimization problemsExposure to forecasting models or simulation-based decision supportFamiliarity with deploying models into production environments or building APIs for model consumptionBackground in applied mathematics, industrial engineering, computer science, or related fields