LMI is seeking a Supply Chain Risk Management (SCRM) Data and Analytics Lead to support the design, development, and implementation of an enterprise SCRM organization for a client in the Washington, DC metro area. The ideal candidate bridges SCRM policy and technical execution, with practitioner-level expertise in data engineering, data science, analytics, and modeling and simulation applied to supply chain risk contexts. This role serves as the technical counterpart to our SCRM strategy and policy capability, translating risk frameworks and business requirements into data pipelines, analytical models, dashboards, and decision-support tools that enable enterprise-wide SCRM operations.

Responsibilities may include:

• Design, develop, and maintain data pipelines that ingest, integrate, and transform supplier, contract, financial, and threat intelligence data from disparate internal and external sources.
• Define and implement data schemas, governance structures, and quality processes that support reliable, auditable SCRM analytics and reporting at enterprise scale.
• Develop quantitative supplier risk scoring models, criticality assessments, and risk segmentation frameworks using statistical and machine learning methods.
• Apply NLP and text analytics to extract risk signals from unstructured sources including news feeds, regulatory filings, and threat intelligence reports.
• Design and develop modeling and simulation frameworks to assess supply chain disruption scenarios, single-point-of-failure risks, and mitigation trade-offs, including scenario-based tools to support wargaming and executive decision-making.
• Build and maintain executive-ready dashboards, geospatial visualizations, and automated reporting pipelines that communicate SCRM risk posture clearly and actionably.
• Partner with SCRM strategy and policy experts to translate risk frameworks and governance requirements into technical data and analytical solutions.
• Define technical requirements for SCRM tools, platforms, APIs, and data integrations and support vendor evaluation and capability assessments.
• Facilitate working sessions with acquisition, cybersecurity, IT, data, and mission operations stakeholders to define data requirements, reporting needs, and analytical use cases.
• Prepare technical documentation, data dictionaries, model methodology briefs, and decision-ready products for both technical and senior non-technical audiences.

MINIMUM QUALIFICATIONS

• Undergraduate degree required; quantitative discipline preferred (data science, computer science, statistics, mathematics, operations research, or systems engineering).
• Seven (7) or more years of relevant experience in data engineering, data science, analytics, or quantitative modeling.
• Proficiency in Python, R, or similar languages for data manipulation, statistical analysis, and model development.
• Experience building and deploying data pipelines, ETL/ELT processes, or data integration solutions in enterprise environments.
• Proficiency with SQL and familiarity with data warehouse or data lake architectures (e.g., Snowflake, Databricks, Redshift, Synapse, or equivalent).
• Demonstrated experience developing quantitative risk models, supplier scoring frameworks, or anomaly detection capabilities.
• Experience designing and building dashboards and data visualizations in Tableau, Power BI, or equivalent platforms.
• Familiarity with SCRM concepts including supplier risk assessment, third-party due diligence, supplier segmentation, and criticality analysis.
• Strong written and verbal communication skills; ability to produce technical documentation and translate analytical findings into executive-ready outputs.
• Active TOP SECRET clearance required. Must be a U.S. citizen.

PREFERRED QUALIFICATIONS

• Experience with M&S tools or frameworks (e.g., AnyLogic, Simio, Arena, agent-based modeling platforms, or custom Python/R stochastic simulation development).
• Familiarity with federal SCRM policy frameworks including NIST SP 800-161, DFARS 252.204-7012, and related DoD acquisition risk guidance.
• Experience working in or supporting DoD or federal agency data environments, including familiarity with data classification, access controls, and ATO/RMF processes.
• Knowledge of graph analytics or geospatial analysis methods applied to supplier network mapping and geographic concentration risk.
• Familiarity with commercial supply chain risk data providers (e.g., Exiger, Sayari, Dun & Bradstreet) or open-source threat intelligence tools.