Senior Data Scientist

Element14 is hiring a Senior Data Scientist to do the analytical core of our financial analytics work for a public sector organization focused on housing and community development. You will own real questions end-to-end — from raw transactional data through to models and analytical products that program staff and leadership use to make better decisions. This is not an academic seat. We are looking for someone who has shipped real work on hard problems, can defend their methods, and would rather solve a problem that matters than write the perfect paper about it.

What you would work on

Element14 is building a production financial analytics capability for a public sector organization focused on housing and community development. The organization manages a large grant and program portfolio, and the team's job is to help leadership and program offices understand it — where the money is going, how programs are performing, where the patterns in the data warrant attention, and what the modeling work can do to support better decisions.

The team produces three flavors of analytics, each with its own users and its own data. Portfolio analytics gives leadership the macro view of the program landscape. Entity analytics gives program officers and analysts the per-firm view that supports oversight and program management. Transaction analytics gives operating staff a per-payment view at the point of action. We start producing real value on day one, using the organization's own data and public sources.

The work is data science and engineering applied to financial data. Building statistical and ML models on disbursement and recipient data. Designing analytical products that program staff and leadership actually use. Connecting the organization's internal systems to public datasets in ways that reveal patterns the organization could not see on its own. Careful, defensible work that holds up under scrutiny.

We are AI-first by default. Modern data science and ML are part of the toolkit on every engagement — LLMs for parsing unstructured documents and free-text fields, ML for scoring and classification, agentic workflows for repetitive analytical work. We are not chasing AI for its own sake, but we are not doing 2018-era data science either. We expect the people on this team to be fluent in current tools and to use them to be faster and sharper than the consulting median.

The data sources span the organization's own systems, commercial data, and public records. Organizational systems include the general ledger, grant tracking, and program disbursement systems. Commercial sources include major entity and identity data providers. Public sources include USASpending.gov, FFATA sub-awards, SAM.gov, and IRS Form 990s. The interesting analytical questions almost always live at the intersection.

Beyond this engagement, we expect this team to grow with the firm. As Element14 wins additional federal and state work, the people we hire now will help shape future engagements and the capabilities we build.

What you will do

• Build statistical and ML models on the organization's financial and program data. Models that describe what is happening in the portfolio, models that predict where attention is most needed, models that explain variation across programs and recipients. Make the factors interpretable to a program officer who needs to act on the output.
• Apply classical statistical methods alongside modern ML. Time series analysis on disbursement patterns, regression and causal inference where they fit, supervised and unsupervised methods where they earn their keep. Choose the right tool for the question, not the trendiest one.
• Work with the full data landscape — internal transactional data, commercial entity and identity data, public records. Cross-reference, link, and reason across sources. Entity resolution is part of the craft; the same legal entity often appears under multiple identifiers across programs, and the same recipient appears in different forms across datasets.
• Use modern AI tooling as a standard part of the workflow. LLMs to extract structure from PDFs, narratives, and free-text fields; agentic workflows for repetitive analytical work; ML for scoring and classification. We expect fluency here, not curiosity.
• Build analytical products people actually use. Models that get embedded in workflows. Dashboards that get checked. Findings that get acted on. The full loop from data to decision, not just the modeling step in the middle.
• Engage directly with subject matter experts to validate analytical work against operational reality. Knowing the program well enough to recognize when the data is telling you something real — and when it is telling you something that is technically true but operationally meaningless.

Who we are looking for

In addition to the qualities we look for in everyone on the team:

• Genuine interest in public service. You are excited about helping government agencies operate more effectively and advance their mission. The variety appeals to you — one quarter you might be working on financial integrity for housing programs, the next on agricultural data, the next on health programs.
• Commitment to technical craft. You stay current. You can point to tools and techniques you have picked up in the last twelve months and explain why they matter. Cloud, data, and AI are central to what we do, and we expect that to be central to how you think too.
• Strong communication and relationship-building. You build trust with government stakeholders by listening carefully, explaining clearly, and following through. You learn the program — not just the data — deeply enough that clients want you in the room.
• Drive and ownership. This is not a clock-in, clock-out role. We want people who care, and that shows up in their work — anticipating the next question, fixing what is broken before being asked, and treating the mission as their own.
• Strong analytical foundations. Five or more years of applied data science experience, with comfort doing analytical work in a cloud environment. Strong Python (pandas, scikit-learn, NetworkX, statsmodels, or equivalents) and SQL. Solid grounding in statistics — you can pick the right tool for the question and explain why.
• Methods range. Real depth in at least one of: statistical modeling, time series analysis, supervised and unsupervised learning, entity resolution, or graph and network analysis on transactional data. Curiosity about the rest.
• Applied, not academic. You like real problems, real stakes, and real answers. You can read a paper from last week, decide whether it is worth trying, and either ship it or set it aside without ego. Pure researchers are not what we are after — we want people who roll up their sleeves and solve hard problems that matter.
• AI-first. Comfortable working with modern LLMs and agentic tooling as part of your daily workflow. You have opinions about where they help and where they do not, and you use them to be faster, not flashier.
• Care for the work. Penny-exact when the work calls for it. You catch your own mistakes, document your assumptions, and would rather slow down than ship something brittle.
• Preferred: evidence of work you have done outside a paid job — open-source contributions, side projects, academic research, hackathons, or a portfolio you can walk us through. We read these as signals of curiosity and craft.
• Also a plus: experience with risk modeling in any sector; experience with government financial data or public datasets like USASpending.gov, SAM.gov, FFATA, or FinCEN BOI; familiarity with AWS data services; experience with Databricks; experience with entity resolution at scale (Splink, Senzing, or comparable).
• Required: ability to obtain a U.S. Federal Public Trust clearance. This requires U.S. citizenship or lawful permanent residency and a successful background investigation.

Salary range: $160,000 – $200,000 annual base. Final offer determined based on experience, depth, and the specific seat. We post ranges because we believe in transparency about pay.

Location: Hybrid — Washington, DC metro area preferred for periodic on-site collaboration with the client. Remote candidates within the United States considered for the right fit. U.S. work authorization required.

Perks & benefits

• Health, dental & vision coverage. Comprehensive medical plans with generous company support toward your premiums.
• 401(k) retirement plan. Save for the future, pre-tax.
• Remote-friendly across the U.S. Work from anywhere in the United States, with periodic on-site collaboration in the DC metro area where the work calls for it. We value autonomy and trust.
• Mission-driven work. Meaningful projects that change how government uses data and technology in service of the public.
• Small, tight-knit team. A senior bench where your ideas matter and your growth is encouraged.

Element14 is an equal opportunity employer. We are committed to building a team that reflects the public we serve.