Decision Scientist

This is a fully remote position, open to applicants in United States.

📋 Description

• Develop and improve mathematical optimization, simulation, time-series, statistical/ML, and network-based analytical models.

• Formulate analytical problems, including decision variables, objective functions, constraints, assumptions, handling infeasibility, and trade-offs.

• Utilize Python analytical pipelines and Databricks notebooks; trace dependencies across data transformations, model logic, and downstream results.

• Prepare and validate analytical inputs derived from demand forecasts, inventory, bills of materials, supplier data, and other operational datasets.

• Build, train, and assess ML models for classification, forecasting, anomaly detection, and ranking tasks.

• Validate solutions through testing, scenario and sensitivity analysis, and reconciliation of business results.

• Write modular, maintainable, and testable Python code; document model logic, implement regression tests, use version control, and troubleshoot data, model, and runtime issues.

• Collaborate with Decision Science, Data Engineering, and Technology teams regarding implementation dependencies, testing, and production readiness.

• Independently resolve well-defined technical challenges and articulate approaches, assumptions, and outcomes clearly.

⛳️ Requirements

• Master's degree in Operations Research, Industrial Engineering, Applied Mathematics, Statistics, Computer Science, or a related quantitative field.

• 4+ years of practical experience in developing analytical/OR/ML solutions, preferably within supply chain, manufacturing, logistics, procurement, or similar operational settings.

• Strong foundation in optimization modeling, encompassing LP/MILP formulation, solver behavior, and practical handling of infeasibility.

• Hands-on experience with applied ML/statistical modeling, including feature engineering, model training, appropriate validation design, metric selection, and interpretation of results.

• Proficiency in Python, including familiarity with pandas, NumPy, and analytical libraries.

• Experience in writing maintainable code that extends beyond exploratory prototypes.

• Practical experience with Pyomo and LP/MILP solvers such as GLPK or Gurobi.

• Capability to understand, debug, and extend existing code and analytical models.

• Ability to communicate technical designs and results effectively to the project team.

• Preferred: Familiarity with Azure Databricks, PySpark/Spark, Delta Lake, and Unity Catalog.

• Preferred: Experience in supply network modeling, multilevel bills of materials, inventory optimization, disruption simulation, or graph-based analysis; knowledge of Neo4j/Cypher is a plus.

• Preferred: Experience in time-series forecasting, anomaly detection, and ML ranking/recommendation methods.

• Preferred: Background in pharmaceutical/life sciences supply chains or exposure to GenAI-enabled analytics and conversational insights.

🏝️ Benefits

• Remote work arrangement.