Data Scientist

CLEARANCE REQUIREMENT: Candidates must be eligible to obtain and maintain a U.S. Secret or Top Secret security clearance. This is a non-negotiable, hard requirement driven by our Department of War contractor obligations. Candidates who are not eligible will not be considered for this role.

Job Overview: 

Sustainment is scaling fast. Our engineering team has grown significantly and our delivery surface spans commercial SaaS product development and government-funded R&D programs -  including work supporting the U.S. Department of War.

We are looking for a Data Scientist who operates at the frontier of that data: collecting it, making sense of it when it's messy and unstructured, building models that drive product and business decisions, and owning high-impact analytics end-to-end. This role lives at the intersection of data science and data strategy. You're equally comfortable wrangling real-world data as you are designing experiments, building predictive models, and working with others to deploy data driven Machine Learning systems.

The ideal candidate gets excited by ambiguous, real-world data problems, wants to learn the business context behind them, and can zoom out from a dataset to ask: "what should we be collecting, modeling, and why?" You'll work across Product, Engineering, and Operations to expand what Sustainment knows about the U.S. industrial base and translate that knowledge into outcomes.

What you'll own: 

• Lead end-to-end data heavy projects through product, growth, operations, and customer experience; from problem framing through deployment and measurement
• Identify, evaluate, and connect to new sources of unstructured and semi-structured data relevant to U.S. manufacturing
• Build and maintain reporting pipelines and dashboards that translate raw data into clear analytics for stakeholders
• Design and analyze experiments including A/B testing, causal inference, and uplift analysis
• Develop scalable datasets, metrics, and analytical frameworks
• Develop and document data collection strategies and thinking systematically about coverage, quality, and freshness across Sustainment's data assets
• Apply AI-assisted workflows to enrich, normalize, and classify unstructured data
• Partner with Product and Engineering to operationalize models, software, and insights
• Collaborate with product and engineering to tie data quality directly to product outcomes: match rates, recommendation precision, and profile completeness
• Maintain and improve Sustainment's data quality standards by identifying gaps and driving enrichment initiatives
• Mentor junior and mid-level data practitioners and help shape data quality, governance, and experimentation best practices
• Present findings and recommendations to cross-functional stakeholders and produce strategy documents that inform quarterly prioritization

What we're looking for: 

• [REQUIRED] Must be eligible to obtain and maintain a U.S. Secret or Top Secret security clearance
• 4+ years of experience in Data Science, Applied ML, Analytics, or a related data-focused role
• Strong proficiency in Python and SQL
• Demonstrated experience working with unstructured or semi-structured data 
• Experience with experimentation, statistical analysis, and predictive modeling
• Comfortable building dashboards and reporting layers 
• Active user of AI tools (Claude, ChatGPT, or similar) for data workflows
• Familiarity with production ML systems and modern data tooling
• Business intuition with the ability to connect analysis to outcomes and ask strategic questions about data coverage, quality, and collection
• Experience working cross-functionally with Product and Engineering teams

Nice to have: 

• Background in or adjacent to manufacturing, defense, aerospace, or industrial sectors (strong preference)
• Experience with NLP, entity resolution, or AI-assisted classification pipelines
• Exposure to vector databases or semantic search concepts
• Experience with LLMs or Generative AI applications
• Familiarity with modern cloud and data platforms (Snowflake, Databricks, AWS, GCP)
• Experience building self-serve analytics or experimentation platforms
• Experience working in a Series A/B startup environment