Job Title

We are looking for aData Scientist with deep expertise in optimization and applied operations researchto 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 stakeholdersRequired Skills & Experience: 5+ years of experience in data science, with strong focus on optimization or operations research Proficient in Python, relevant libraries and tools: Optimization : Pyomo, PuLP, OR-Tools, cvxpy Data Science : pandas, NumPy, scikit-learn, matplotlib/seaborn Tools : Gurobi: CPLEX, or Google OR-Tools Hands-on experience with linear programming, mixed integer programming, or constraint programming Ability to structure, clean, and analyze complex datasets Strong communication skills for explaining technical models to diverse audiencesPreferred (Nice to Have): Experience working on scheduling, routing, or supply chain optimization problems Exposure to forecasting models or simulation-based decision support Familiarity with deploying models into production environments or building APIs for model consumption Background in applied mathematics, industrial engineering, computer science, or related fields