Data Science

Elliptic is the leader in digital asset decisioning, we have built the most comprehensive platform for efficiently extracting crypto data and intelligence across blockchains with the greatest accuracy.

Our platform’s unrivalled uptime, scalability, depth and breadth of our data and intelligence means exacting organizations choose Elliptic for their compliance, risk management, intelligence operations and blockchain infrastructure needs.

Founded in 2013, Elliptic is headquartered in London with offices in New York, Washington D.C., UAE, Singapore and Tokyo. To learn more, visit www.elliptic.co and follow us on LinkedIn and X.

Do you want to lead a data science team on a problem the industry has not solved yet, while still writing code yourself? Are you excited about building the data foundation for how compliance works when the customer is an autonomous agent rather than a person? Are you looking for a values led company where a small team is trusted with a question that matters commercially in months, not years?

You will lead data science inside Elliptic's Intelligence team, alongside Intelligence Collection, Research and Investigations, and Professional Services. This is deliberately a player-coach role with a prospect of scaling a team. At first you will lead a small team of data scientists and you are expected to own the team's direction, performance and growth. You will own the evidence for the models your team builds, and you will represent them at Elliptic's model risk governance forum. This is not a compliance tax bolted onto the technical work: it is the reason institutions trust our outputs.

The reason we are hiring a lead as we add more data scientists is that a small team working on an unsolved problem needs someone who intrinsically understands the domain, not a layer of management above it. You will set what this team works on, hire the next people into it, and be the person whose technical judgement the team trusts because you can do the work yourself.

• Want to lead a team without leaving the technical work behind, and can say honestly where you draw that line.
• Are comfortable being accountable for a question nobody has answered yet, including for saying what you do not know.
• Treat management as the work rather than as overhead: objectives, feedback, development and difficult conversations included.
• Treat governance as part of building good models rather than as paperwork that follows them, and would rather surface a problem with your own model than have someone else find it.
• Are pragmatic about trade offs between rigour and speed, and can explain the trade off you chose.
• Make other teams more effective rather than treating them as consumers of your team's output.
• Are curious about how the company operates beyond your own function.
• Care about the mission of using intelligence to fight financial crime and protect the global financial system.
• AI fluency, which is essential for this role. You must be able to demonstrate how you apply AI tools and approaches within a data science workflow: writing and debugging code, exploring unfamiliar data, accelerating pipeline and model work, and automating repetitive tasks. You must also be able to show how you critically evaluate the output, where you would not rely on it, and how you set verification expectations for a team rather than only for yourself. This is assessed in a dedicated stage of the interview process, working with your own tooling.
• 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 data science and machine learning capability that is current. Strong Python and SQL, and the ability to interrogate large behavioural or transactional datasets yourself and 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 ours: 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 that each person knows why their work matters.
• Based in Washington, D.C. or willing to relocate there.
• US citizenship
• 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 the unglamorous parts: 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, or as a vendor to it, or in another domain with equivalent assurance requirements.

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We know Diversity and Inclusion is much deeper than just hiring, but it’s important for us to mention it here. We welcome and embrace individuals of all backgrounds and identities at Elliptic, and this is an ongoing priority for us. We know incredible people don’t all think in the same way. We want to be challenged every day. We believe our diverse team of individuals underpins this by bringing creative thinking and innovation to Elliptic every day. We are committed to creating a diverse, inclusive and equitable workplace, so we welcome applications from everyone, even if you may not think you fit all of the requirements of our roles. We foster an environment of psychological safety, where everyone feels comfortable to bring their whole self to work.