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Elliptic is the leader in digital asset decisioning, providing the most comprehensive platform for extracting crypto data and intelligence across blockchains. Founded in 2013 and headquartered in London, the company serves compliance, risk management, intelligence operations, and blockchain infrastructure needs for exacting organizations globally.
You will lead the data science team within Elliptic's Intelligence function, working alongside Intelligence Collection, Research, Investigations, and Professional Services. This is a player-coach role with the prospect of scaling the team. You will lead a small team of data scientists, own the team's direction, performance, and growth, and represent the team's models at Elliptic's model risk governance forum.
Key responsibilities include:
- Lead and grow the team: set objectives aligned with company strategy, own performance and development, manage difficult conversations, and build development paths using Elliptic's Intelligence career framework.
- Hire the next data scientists: own the hiring process end-to-end, including levels, job descriptions, hiring plan, interview panel, and hiring decisions. Scale the team to seven to nine people over time.
- Build the collection for the future of compliance: define specifications, sequence work, set quality bars, and ensure datasets are well-understood.
- Own model governance: maintain model inventory, ensure validation and testing evidence exists at required tiers, monitor for drift, submit material changes and deployments for review, and remediate issues to agreed deadlines.
- Lead data science research: frame research questions, run initial experiments, form defensible views of Elliptic's capabilities and timelines, and present recommendations to Intelligence leadership, the Chief Scientist, and Product.
- Stay technically hands-on: write code, review team work at the method level, and make critical method calls yourself.
- Own existing team commitments: ensure reliability of current work on clustering, attribution, heuristics, and labelling; decide what to stop.
- Work across functions: partner with Collection, Collection Engineering, Research, Investigations, the Chief Scientist, Product, and Engineering to deliver platform capabilities.
- Set the AI working standard: decide how the team uses AI in engineering and research workflows, what must be verified, and hold the team to it.
In the first six months, you will deliver: a collection plan at scale with a documented maintenance path; an agreed Elliptic position on compliance system evolution with capability work underway or deliberately deferred; at least one data science hire with existing team levelled and developed; model governance in good standing; agreed Intelligence data science objectives delivering across functions; visible improvement in core output reliability or throughput; and intact technical credibility.
The role is hybrid with the option to work from almost anywhere for up to 90 days per year. Elliptic offers 25 days annual leave plus 8 US public holidays, birthday leave, enhanced parental leave (16 weeks fully paid), comprehensive healthcare, 401k match, mental health support, $1,000 annual L&D budget, and $650 remote work setup budget.
Requirements:
- AI fluency essential: demonstrate how you apply AI tools and approaches within data science workflows (writing/debugging code, exploring data, accelerating pipeline and model work, automating tasks); critically evaluate output and set verification expectations for a team. This is assessed in a dedicated interview stage using your own tooling.
- Demonstrated experience managing data scientists or machine learning engineers: setting objectives, owning performance including underperformance, developing people. You can point to someone whose career changed because of your management.
- Experience hiring into technical teams, including defining the bar rather than only sitting on panels.
- Deep, hands-on data science and machine learning capability that is current: strong Python and SQL; ability to interrogate large behavioural or transactional datasets and defend your method under challenge.
- Track record of taking ambiguous questions to delivered datasets, models, or capabilities, including deciding what was good enough.
- Experience with modern data stack comparable to Elliptic's: cloud data lake, Spark or Databricks, AWS.
- Clear communication with technical and non-technical stakeholders; ability to cascade direction so each person knows why their work matters.
- Based in Washington, D.C. or willing to relocate there.
- US citizenship.
Bonus qualifications:
- Work on agentic systems, LLM agents, or autonomous transaction flows in compliance, payments, fraud, or infrastructure.
- Experience building large datasets or collections from nothing, including schema decisions, quality checks, and maintenance.
- Blockchain or on-chain data experience: clustering, attribution, heuristics, anomaly detection, mempool analysis, or privacy-preserving systems research.
- Payments, fraud, or risk data science experience where false negatives have commercial cost.
- Experience growing a team from a few people to a full department.
- PhD or equivalent research training, or record of published applied research.
- Experience in regulated environments or with model risk management frameworks in financial services or equivalent assurance domains.
