Decision Scientist

🔥 3 minutes ago

🇺🇸 United States – Remote

⏰ Full Time

🟡 Mid-level

🟠 Senior

👻 Ghost score 10%

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Virtusa

WebsiteLinkedInAll Job Openings

10,000+ employees

Founded 1996

💼 Consulting

🏢 Enterprise

🤖 Artificial Intelligence

💰 $108M Post-IPO Equity - Virtusa on 2017-05

Consulting • Enterprise • Artificial Intelligence

Virtusa is a global technology services and consulting company that helps enterprises reimagine their business through digital engineering, AI-driven and domain-focused solutions. It offers consulting, design, engineering, automation, analytics and optimization services, and partners with organizations across banking & financial services, communications, healthcare, insurance, retail, manufacturing and other industries to deliver enterprise-scale technology and AI solutions. Virtusa emphasizes AI services, domain-driven solutions for the AI enterprise, and end-to-end digital transformation.

📋 Description

• 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

• 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

• Remote work arrangement

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