Data Science Lead

As Data Science Lead at Elliptic, you will lead the data science team within the Intelligence function, owning both team growth and hands-on technical work. You report into Intelligence leadership and work across Intelligence Collection, Research, Investigations, and Professional Services to build the data foundation for how compliance operates when the customer is an autonomous agent rather than a person. This is a player-coach role where you set team direction, hire, and stay technically credible.

What you'll do

• Lead and grow the data science team: set objectives aligned with company strategy, own performance and development, manage underperformance directly, and build development paths within Elliptic's Intelligence career framework.
• Hire the next data scientists, owning the process end to end: define levels and job descriptions, build the hiring plan, lead the interview panel, set the bar, and make hiring decisions.
• Build the collection for the future of compliance: translate strategic direction into specification, sequence the work, set quality standards, and be clear about what the dataset does and does not support.
• Own model governance for your team's models: keep the model inventory current and correctly tiered, ensure validation and testing evidence exists at the required standard, monitor for drift, act on threshold breaches, and remediate issues to agreed deadlines.
• Lead Elliptic's data science research direction: frame the research question, run initial experiments, form a defensible view of what Elliptic needs to build and when, and bring that view to Intelligence leadership, the Chief Scientist, and Product as a clear recommendation.
• Stay technically hands on: write code, review your team's work at the level of method, and make method calls yourself when they matter.
• Own the team's existing commitments on clustering, attribution, heuristics, and labelling: ensure reliability, manage trade-offs, and decide what to stop.
• Work across functions: partner with Intelligence Collection, Collection Engineering, Research, Investigations, the Chief Scientist, Product, and Engineering to deliver data science work as platform capability rather than unused analysis.
• Set the AI working standard for the team: decide how the team uses AI in engineering and research workflows, determine what must be verified and how, and hold the team to it.

What you bring

• AI fluency essential to this role: you can demonstrate how you apply AI tools and approaches within data science workflows (writing and debugging code, exploring unfamiliar data, accelerating pipeline and model work, automating tasks), and you critically evaluate output, knowing where not to rely on it and how to set verification expectations for a team.
• Demonstrated experience managing data scientists or machine learning engineers: setting objectives, owning performance including underperformance, and developing people. You can point to someone whose career changed because of how you managed them.
• Experience hiring into a technical team, including defining the bar rather than only sitting on panels.
• Deep, hands-on current data science and machine learning capability: strong Python and SQL, the ability to interrogate large behavioural or transactional datasets yourself, and the ability to defend your method under challenge.
• A track record of taking an ambiguous question to a delivered dataset, model, or capability, including deciding what was good enough.
• Experience with a modern data stack comparable to Elliptic's: a cloud data lake, Spark or Databricks, and AWS.
• Clear communication with technical and non-technical stakeholders, and the ability to cascade direction to a team so each person knows why their work matters.
• Based in Washington, D.C. or willing to relocate there.
• US citizenship.

Nice to have

• Work on agentic systems, LLM agents, or autonomous transaction flows, whether in compliance, payments, fraud, or infrastructure.
• Experience building a large dataset or collection from nothing, including schema decisions, quality checks, and maintenance.
• Blockchain or on-chain data experience: clustering, attribution, heuristics, anomaly detection, mempool analysis, or research into privacy-preserving systems.
• Experience in payments, fraud, or risk data science, where the cost of a false negative is commercial rather than academic.
• Having grown a team from a few people to a full department, and being able to describe what broke on the way.
• A PhD or equivalent research training, or a record of published applied research.
• Experience in a regulated environment or with model risk management frameworks, whether in financial services, as a vendor to it, or in another domain with equivalent assurance requirements.

What we offer

• Hybrid working: option to work from almost anywhere for up to 90 days per year.
• Remote work budget of $650 to set up your home office space.
• Annual Learning and Development budget of $1,000 (agreed with your manager) to contribute to your growth and development.
• 25 days of annual leave plus 8 US Public Holidays, plus an extra day off for your birthday.
• Comprehensive medical, dental, and vision coverage through a range of providers (including Tufts, Kaiser, Aetna, UHC, and Blue Shield of CA) with generous premium contributions for you and your dependents.
• 401k with company match.
• Full access to Spill mental health support.

About Elliptic

Elliptic is the leader in digital asset decisioning, with the most comprehensive platform for extracting crypto data and intelligence across blockchains. Founded in 2013 and headquartered in London, Elliptic has offices in New York, Washington D.C., UAE, Singapore, and Tokyo. Institutions choose Elliptic for compliance, risk management, intelligence operations, and blockchain infrastructure needs.