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The Supply Chain Data Scientist is a key contributor to the team developing decision‑support technologies for Comcast’s Demand & Supply Planning and Network Optimization functions. The role centers on Operations Research, mathematical optimization, and network design, leveraging tools such as Mixed‑Integer Linear Programming, simulation, and heuristic algorithms. Artificial Intelligence and Machine Learning are used where appropriate to support forecasting, demand sensing, and predictive analytics, complementing the core optimization solutions.
Job Responsibility:
Design and optimize Linear Programming, Mixed‑Integer Programming, and Simulation models for Supply Chain network, capacity, and inventory decisions
Develop and evaluate advanced optimization and OR techniques to solve complex business problems
Perform data wrangling, programming, and statistical analysis to support optimization and ML initiatives
Build and validate ML models that enhance forecasting, classification, and decision‑making
Create training materials and documentation to help teams apply optimization and analytics tools
Monitor Supply Chain KPIs to identify opportunities to refine optimization and statistical models
Requirements:
5+ years of advanced Supply Chain Data Science experience, ideally within Telecom, CPG, or retail replenishment
Master’s degree in operations research, Statistics, Supply Chain, Engineering, Computer Science, or related discipline
Strong experience in Supply Chain Network Optimization and Inventory Optimization, including mathematical modeling and scenario evaluation
Proficiency with optimization tools such as Gurobi, AMPL, or similar solvers/modeling frameworks
Experience with BI/reporting tools such as Tableau, Power BI, or QlikView
Proven ability to apply data science and optimization techniques to deliver measurable business impact
Exceptional communication and collaboration skills, able to partner effectively with technical and business teams
Experience in consumer goods or retail environments leveraging advanced modeling to drive operational outcomes
Flexibility to collaborate with US based teams and support critical work aligned to U.S. time zones
Knowledge of demand planning, forecasting processes, and related metrics (forecast accuracy, bias, demand attainment) is a plus
Nice to have:
Knowledge of demand planning, forecasting processes, and related metrics (forecast accuracy, bias, demand attainment)