Decision Scientist

Virtusa

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

Posted 11/12/2026full-timeRemote • United StatesMid-LevelSeniorWebsite

Core Competencies

Role fit

Core Competencies

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Demonstrates expertise in developing and enhancing mathematical optimization and machine learning models, with strong proficiency in Python and analytical libraries. Capable of formulating complex analytical problems and collaborating effectively with cross-functional teams to deliver actionable insights.

Highest-signal resume keywords

Python ProficiencyMathematical OptimizationMachine Learning Model DevelopmentAnalytical Problem FormulationExperience with LP/MILP Solvers

ATS Keywords

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Applicant Tracking System Keywords

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Hard Skills

Mathematical OptimizationStatistical ModelingMachine LearningFeature EngineeringModel TrainingValidation DesignLP/MILP FormulationDebugging Existing CodeData TransformationSensitivity Analysis

Soft Skills

Clear CommunicationProblem SolvingCollaboration

Tools & Technologies

PythonDatabricksPyomoGLPKGurobiPandasNumPyPySparkDelta LakeNeo4j

Industry Keywords

Supply ChainManufacturingLogisticsOperations ResearchPharmaceutical Supply ChainsDisruption SimulationTime-Series ForecastingAnomaly DetectionGraph-Based AnalysisGenAI-Enabled Analytics

Tech Stack

Tools & technologies

AzureNeo4jNumpyPandasPySparkPythonSparkUnity

About the role

Key responsibilities & impact

Develop and enhance mathematical optimization, simulation, time-series, statistical/ML, and network-based analytical models
Formulate analytical problems, including decision variables, objective functions, constraints, assumptions, infeasibility handling, and trade-offs
Work with Python analytical pipelines and Databricks notebooks; trace dependencies across data transformations, model logic, and downstream outputs
Prepare and validate analytical inputs from demand forecasts, inventory, bills of materials, supplier data, and other operational datasets
Develop, train, and evaluate ML models for classification, forecasting, anomaly detection, and ranking
Validate solutions through testing, scenario and sensitivity analysis, and business-result reconciliation
Write modular, maintainable, 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 on implementation dependencies, testing, and production readiness
Independently solve well-defined technical problems and clearly explain approaches, assumptions, and results

Requirements

What you’ll need

Master's degree in Operations Research, Industrial Engineering, Applied Mathematics, Statistics, Computer Science, or a similarly quantitative field
4+ years of hands-on experience building analytical/OR/ML solutions, ideally in supply chain, manufacturing, logistics, procurement, or other operational environments
Strong fundamentals in optimization modeling, including LP/MILP formulation, solver behavior, and practical infeasibility handling
Hands-on applied ML/statistical modeling experience, including feature engineering, model training, appropriate validation design, metric selection, and interpretation of results
Strong Python proficiency, including pandas, NumPy, and analytical libraries
Experience writing maintainable code beyond exploratory prototypes
Hands-on experience with Pyomo and LP/MILP solvers such as GLPK or Gurobi
Ability to understand, debug, and extend existing code and analytical models
Ability to communicate technical design and results clearly to the project team
Preferred: Experience with Azure Databricks, PySpark/Spark, Delta Lake, and Unity Catalog
Preferred: Background in supply network modeling, multilevel bills of materials, inventory optimization, disruption simulation, or graph-based analysis; Neo4j/Cypher is a plus
Preferred: Experience with time-series forecasting, anomaly detection, and ML ranking/recommendation methods
Preferred: Experience in pharmaceutical/life sciences supply chains or exposure to GenAI-enabled analytics and conversational insights

Benefits

Comp & perks

Remote work arrangement