Job description

SAIC is seeking a Subject Matter Expert (SME) – Data Scientist Lead to support the U.S. Transportation Command (USTRANSCOM) Joint Distribution Process Analysis Center (JDPAC V) at Scott AFB, IL. This role serves as the lead data scientist and principal technical advisor guiding advanced analytics and data engineering initiatives across the Department of Defense (DoD) sustainment supply chain and global distribution enterprise. As USTRANSCOM transitions its analytical backbone from legacy on-premises systems (Teradata) to modern cloud environments—including the Palantir / Maven Smart System and AWS / War Data Platform—the SME – Data Scientist Lead will spearhead the architecture, development, and operationalization of advanced analytical models and decision-support tools. This position blends technical leadership, hands-on data science, and stakeholder collaboration to deliver actionable intelligence for global mobility, deployment, and distribution operations. Key Responsibilities: • Technical Leadership & Cloud Modernization • Serve as the lead data science SME, advising USTRANSCOM leadership and JDPAC stakeholders on analytic strategy, modern data architecture, and cloud adoption. • Drive the migration of legacy analytic workflows and pipelines (Teradata) into enterprise cloud environments, primarily Palantir (Maven Smart System) and AWS (War Data Platform). • Translate complex operational, distribution, and supply chain questions into rigorous mathematical models, predictive algorithms, and automated analytical pipelines. • Data Engineering & Pipeline Development • Design, implement, and govern data pipelines and ontologies across AWS and Palantir environments. • Ingest, harmonize, and curate disparate, enterprise-scale logistics data from DoD transportation and supply data systems (including IGC and affiliated distribution feeds). • Build robust data transformations and workflows utilizing modern cloud data tools and Power Automate to streamline recurring reporting processes. • Data Science, Machine Learning & Operations Research • Formulate and deploy advanced statistical, operations research, and machine learning addressing sustainment supply chain readiness, capacity constraints, and network distribution dynamics. • Develop predictive and prescriptive capabilities, including: • Multi-echelon supply and transportation demand forecasting • Network and multimodal route optimization • Port throughput and distribution bottleneck analysis • Supply chain vulnerability, risk, and resilience modeling • Deliver operational prototypes and simulation tools that evaluate time, cost, asset visibility, and mission trade-offs under dynamic operational conditions. • Business Intelligence, Visualization & Automation • Build and deliver interactive decision-support applications, dashboards, and exploratory tools using Palantir Workshop, Contour, Quiver, Qlik, Databricks, and Microsoft Power BI. • Implement automated data alerts, workflows, and process integrations using Power Automate. • Deliver high-impact executive briefings, technical documentation, and visual narratives to senior military and civilian leaders. • Team Mentorship & Delivery Management • Lead and mentor a team of data scientists, operations research analysts, and data engineers in modern analytics engineering, version control, and collaborative development best practices. • Coordinate cross-functional requirements with functional logistics SMEs, data stewards, and cloud engineers in an agile delivery framework. Required Qualifications: • Clearance & Citizenship: Active DoD Secret clearance; U.S. Citizenship required. • Education & Experience: Bachelor’s degree in Data Science, Operations Research, Mathematics, Statistics, Computer Science, Systems Engineering, or related technical field, with 10+ years of progressive experience in data science, advanced analytics, or operations research (or Master’s degree with 8+ years). • Cloud & Data Systems: • Demonstrated proficiency in SQL and relational database concepts, including experience with enterprise data architectures and migration patterns (e.g., Teradata to cloud repositories). • Hands-on experience working in modern cloud analytical ecosystems such as AWS/Palantir. • Programming & Modeling: • Advanced proficiency in Python for data engineering, statistical modeling, and machine learning. • Practical background applying linear/nonlinear optimization, simulation, network analysis, or predictive modeling to complex operational problems. • BI & Visual Analytics: • Proven experience building self-service dashboards and decision-support tools using Microsoft Power BI, Qlik, and/or cloud-native analytics platforms (e.g., Databricks, Palantir). • Experience automating workflows and notifications using Microsoft Power Automate. • Leadership & Communication: • Demonstrated ability to lead technical teams, manage sprint backlogs, and convey advanced analytical concepts to non-technical operational stakeholders. Desired Qualifications: - Master’s or Ph.D. in Operations Research, Data Science, or Applied Mathematics. - Hands-on experience within the Palantir / Maven Smart System ecosystem (Workshop, Contour, Quiver, Foundry/Ontology management). - Hands-on experience with the AWS / War Data Platform ecosystem. - Familiarity with DoD sustainment supply chain, joint deployment, and transportation systems (e.g., IGC, GATES, GDSS, CMOS, GFM, IBS). - Experience deploying MLOps pipelines within DoD enterprise cloud environments. - Agile certifications or experience leading data science efforts within Agile/DevSecOps frameworks.

Requirements

Must-haves

Technical Leadership & Cloud Modernization
Serve as the lead data science SME, advising USTRANSCOM leadership and JDPAC stakeholders on analytic strategy, modern data architecture, and cloud adoption.
Drive the migration of legacy analytic workflows and pipelines (Teradata) into enterprise cloud environments, primarily Palantir (Maven Smart System) and AWS (War Data Platform).
Translate complex operational, distribution, and supply chain questions into rigorous mathematical models, predictive algorithms, and automated analytical pipelines.
Data Engineering & Pipeline Development
Design, implement, and govern data pipelines and ontologies across AWS and Palantir environments.
Ingest, harmonize, and curate disparate, enterprise-scale logistics data from DoD transportation and supply data systems (including IGC and affiliated distribution feeds).
Build robust data transformations and workflows utilizing modern cloud data tools and Power Automate to streamline recurring reporting processes.
Data Science, Machine Learning & Operations Research
Formulate and deploy advanced statistical, operations research, and machine learning addressing sustainment supply chain readiness, capacity constraints, and network distribution dynamics.
Develop predictive and prescriptive capabilities, including:
Multi-echelon supply and transportation demand forecasting
Network and multimodal route optimization
Port throughput and distribution bottleneck analysis
Supply chain vulnerability, risk, and resilience modeling
Deliver operational prototypes and simulation tools that evaluate time, cost, asset visibility, and mission trade-offs under dynamic operational conditions.
Business Intelligence, Visualization & Automation
Build and deliver interactive decision-support applications, dashboards, and exploratory tools using Palantir Workshop, Contour, Quiver, Qlik, Databricks, and Microsoft Power BI.

Nice-to-haves

Master’s or Ph.D. in Operations Research, Data Science, or Applied Mathematics.
Hands-on experience within the Palantir / Maven Smart System ecosystem (Workshop, Contour, Quiver, Foundry/Ontology management).
Hands-on experience with the AWS / War Data Platform ecosystem.
Familiarity with DoD sustainment supply chain, joint deployment, and transportation systems (e.g., IGC, GATES, GDSS, CMOS, GFM, IBS).
Experience deploying MLOps pipelines within DoD enterprise cloud environments.
Agile certifications or experience leading data science efforts within Agile/DevSecOps frameworks.

Application prep

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Keywords to mirror

awsmachine learningagilesqlpython

Platform: oracle-cloud
Effort: medium