Data Scientist

Sustainment•Austin, TX

About The Position

Sustainment is an AI-native software platform that helps US-based manufacturers easily find and work with the critical suppliers they need to build and manage their supply chains. Our vision is to reimagine American manufacturing as a hyperconnected, secure, and resilient ecosystem of local and regional suppliers who can more easily connect, interact, and do business with the industry and government customers that rely on them. We are a dual-use technology platform that supports both DoD and commercial customers in pursuit of our vision. 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.

Requirements

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 Haves

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

Responsibilities

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