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Agent guide: [/agents.md](https://www.wearedevelopers.com/agents.md). --- # Director of Data & AI - **Company:** TOU, INCORPORATED - **Location:** Washington, DC, United States - **Experience:** Expert - **Salary:** $150,000.0 - $180,000.0 - **Contract:** Permanent contract - **Skills:** Artificial Intelligence, Data Architecture, Data Governance, Data Infrastructure, Data Security, Python (Programming Language), System Availability, Large Language Models, Data Strategy, Data Lakes, Machine Learning Operations, Databricks - **Published:** September 24, 2026 - **Apply:** https://www.thejobnetwork.com/job/director-data-ai-509979006 ## About the Role * 7+ years in data, analytics, or ML roles, with at least 2 years in a leadership or team-building capacity * Direct, hands-on experience with applied AI such as RAG, agents, and LLM evaluation; candidates without this will not be considered * Technically credible: can design data architecture, read and review Python, evaluate AI pipeline quality, and hold substantive technical conversations with engineers * Has built or led a small data team before, in the 2-8 person range; what matters is that you've done it, not the scale * You bring experience working with or alongside systems that impact youth and families - or you're deeply committed to learning about these systems and approaching the work with humility, curiosity, and care. * You're motivated by impact and systems change. You thrive in a fast-growing, evolving organization and see change as an opportunity to innovate, learn, and make things better. Desired / Nice to Have: * Experience with Databricks, Delta Lake, or similar modern data lake platforms * Experience with LLM observability and evaluation platforms (LangFuse or similar) * Background in civic tech, public health, human services, or mission-driven organizations * Experience with data governance or HIPAA-adjacent compliance * Prior experience as a player-coach who remained hands-on while building a team ## Description As the senior leader for data at Think of Us, you'll be a working player-coach: contributing hands-on alongside the existing data team for the first 6-12 months before growing it further. You're not a pure strategist or a pure manager: you've built a data function before and know how to do the work while building the systems around it., Own Data Strategy and Architecture * Define and drive TOU's data strategy and roadmap, anchored on the Lived Experience Engine * Direct the architecture of our Databricks data lake: what goes in each tier, how the data model evolves, and what standards apply * Set standards for data quality, governance, and platform reliability * Translate organizational priorities into data investments and team structure Lead AI Evaluation and Applied Data Science Direction * Build evaluation frameworks (LangFuse plus custom) for TOU's AI pipelines and agents, the systems that tell us whether AI is actually working * Own decisions on when and how TOU goes beyond cloud models: RAG architecture, agent grounding, retrieval quality, and future ML capability * Negotiate the boundary between the data team and Applied AI Engineering on evals and AI data infrastructure * The data team owns the data and evaluation layer that makes agents work; Applied AI Engineers own the engineering of agents themselves Own Governance, Privacy, and HIPAA Compliance * Build and maintain TOU's data governance posture and HIPAA-adjacent compliance standards * Own data access controls, privacy policies, and the ethical framework for lived-experience data * Ensure TOU's handling of sensitive PII from youth in the child welfare system meets legal and ethical standards Build and Manage the Data Team * Directly manage the data team (currently a Sr. Data Engineer and Sr. Applied Data Scientist), and grow the team over time * Build the processes, culture, and norms of TOU's data function * Be IC-first for 6-12 months, with management load growing as the team grows On a Typical Day, You Might… * Give architectural feedback on a new Databricks gold-layer model the Data Engineer is building * Design an evaluation framework for the ConnectMe AI pipeline, defining what "good enough" means for human-vetted output * Meet with program leadership to translate research questions into data roadmap priorities * Review AI pipeline outputs to assess whether resource recommendations are accurate and equitable across demographic groups * Sit with the Sr. Applied Data Scientist on a recent eval, then decide what's ready to ship, what needs another iteration, and what to brief Product on * Draft a data access policy for how lived-experience narratives are stored and accessed internally, * Lead as a player-coach: you are as comfortable doing the work and building the systems as you are growing the people who will own them * Operate in ambiguity: you make sound calls in a resource-constrained, fast-moving environment without waiting for perfect information * Translate across audiences: you move easily between a technical design discussion with engineers and a data-informed conversation with program, product, and policy leaders * Bring judgment and care to sensitive data: you carry the ethical weight of lived-experience data and let it shape how you build, not just what you build * Set a high bar and grow it in others: you establish the standards for data and evaluation quality and build a team that holds them Why This Role Matters