Data Scientist - Healthcare Supply Chain Riskdata

Data Scientist - Healthcare Supply Chain Risk

Location: McLean, Virginia

Work Environment: Hybrid

Exiger is seeking a Data Scientist with expertise in machine learning, NLP/LLMs, and statistical modeling to support mission-critical analytics for public sector customers. This role is ideal for an individual who thrives at the intersection of data science, supply chain risk, and operational decision-making, and can apply advanced analytics to strengthen supply chain resilience, improve risk visibility, and support continuity of operations.

The Data Scientist will develop and operationalize predictive risk models, anomaly detection systems, and supply chain analytics tools, working closely with AI Solutions Engineers and SCRM analysts to ensure seamless integration into customer environments. This individual will also design dashboards and visualizations, conduct large-scale data analysis across supplier networks and product flows, and present actionable insights to operational and leadership stakeholders-aligning advanced analytics with mission outcomes.

• Develop and apply machine learning (ML) and NLP/LLM models to support supply chain risk identification, supplier analysis, and operational decision-making.
• Build predictive risk models to identify and prioritize vulnerabilities across products, suppliers, and supply chain dependencies.
• Conduct statistical analysis on large-scale datasets (e.g., supplier data, product/SKU data, logistics and trade data) to identify anomalies, disruptions, and concentration risks.
• Design and implement risk scoring methodologies that incorporate supplier health, geographic exposure, dependency mapping, and external risk signals.
• Support scenario analysis and modeling to assess the impact of supply-side and demand-side disruptions.
• Collaborate with Forward Deployed Engineers to operationalize models within Exiger's platform and customer workflows.
• Design and maintain dashboards, reports, and data visualizations to communicate insights to both technical and non-technical stakeholders.
• Translate complex analytical outputs into clear, mission-relevant insights for government stakeholders and operational users.
• Partner with SCRM analysts and program leadership to continuously refine models based on real-world risk signals and evolving mission priorities.

• Advanced proficiency in Python (pandas, NumPy, scikit-learn, TensorFlow/PyTorch), SQL, and applied statistics.
• Strong background in machine learning, including anomaly detection, classification, clustering, and predictive analytics.
• Experience working with LLMs (development, fine-tuning, or applied use cases such as entity resolution, classification, or text analysis).
• Experience working with large, complex datasets, ideally related to supply chains, logistics, trade, or third-party risk.
• Familiarity with supply chain risk management (SCRM), third-party risk, or related domains (e.g., procurement, logistics, compliance, or healthcare supply chains).
• Experience building and deploying risk models or scoring frameworks in production environments.
• Proficiency in data visualization tools (Tableau, Power BI, matplotlib, seaborn) to deliver actionable insights.
• Strong communication skills with the ability to present technical findings to non-technical, mission-oriented stakeholders.
• Ability to work cross-functionally with engineers, analysts, and program teams in a fast-paced, delivery-focused environment.
• S. citizenship

• Experience supporting public sector or government customers.
• Familiarity with healthcare supply chains, medical products, or regulated industries.
• Exposure to graph-based data models, knowledge graphs, or network analysis.
• Experience working with external data sources such as trade data, shipping/logistics data, or corporate entity data.

• High-performance culture rooted in accountability, collaboration, and a shared commitment to excellence.
• Discretionary Time Off for all employees, with no maximum limits on time off.
• Industry leading health, vision, and dental benefits.
• Competitive compensation package.
• 16 weeks of fully paid parental leave.
• Flexible, hybrid approach to working from home and in the office where applicable.
• Focus on wellness and employee health through stipends and dedicated wellness programming.
• Purposeful career development programs with reimbursement provided for educational certifications.

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