Data Science Lead

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 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?

The impact you will have:

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.

What you will do:

• Lead and grow the team. Set objectives with your data scientists in the context of company strategy, own their performance and development, and be the manager who has the difficult conversation rather than deferring it. Level the team against Elliptic's Intelligence career framework and build real development paths.

• Hire the next data scientists. Own the hiring process end to end with support: levels, job descriptions, hiring plan, interview panel and bar, and the hiring decision. Team growth to seven to nine people over time depends on you getting this right early.

• Build the collection for the future of compliance. Take it from a directive to a specification to delivered data: define what good looks like, sequence the work, set the quality bar, and be honest 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, make sure validation and testing evidence exists at the standard each tier requires, monitor for drift and act on threshold breaches, and submit material changes and production deployments for review with the testing artefacts attached. Own the remediation of issues raised against your models, to agreed deadlines.

• Lead Elliptic's data science work. Frame the research question, run the first experiments, and form a defensible view of what Elliptic needs to be able to do and by when. Bring that view to Intelligence leadership, the Chief Scientist and Product as a recommendation rather than as an open question.

• Stay technically hands on. Write code, review your team's work at the level of method rather than output, and make the method calls yourself when they matter.

• Own the team's existing commitments. The team's current work on clustering, attribution, heuristics and labelling continues to serve the platform. You own its reliability and its trade offs, and you decide what to stop.

• Work across functions. Partner with Intelligence Collection, Collection Engineering, Research and Investigations, the Chief Scientist, Product and Engineering, so that data science work lands as platform capability rather than as analysis nobody uses.

• Set the AI working standard for the team. Decide how your team uses AI in its engineering and research workflow, what must be verified and how, and hold the team to it.

What you will achieve in the first 6 months:

• The collection plan delivered at a scale that supports real model or heuristic work, with a documented path to maintaining it.

• An agreed Elliptic position on the evolution of compliance systems, timed against expected industry demand, with the first capability work either underway or deliberately deferred with stated reasons.

• At least one data science hire made, and the existing levelled against the career framework with development plans in place.

• Model governance in good standing for everything your team owns: inventory current and correctly tiered, no overdue validations on the highest tiers, monitoring in place with acted-upon thresholds, and no open issues past their remediation deadline.

• Data science objectives for Intelligence agreed and delivering, with other functions able to state what they can expect from the team and when.

• A visible improvement in the reliability or throughput of the team's core output, attributable to a change you made.

• Technical credibility intact: the team takes your method judgements seriously because you can do the work.

You will be a great fit here if you:

• 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