Data Science Lead

• 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.

Ideal Candidate

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 mission of using intelligence to fight financial crime and protect the global financial system.

Requirements

Our ideal candidate has:

• 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.

Bonus Points for:

• 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.

Job Benefits

• Hybrid Working: The option to work from almost anywhere for up to 90 days per year.
• Remote Working Budget: £500 to set up your home office space.
• Learning & Development: $1,000 budget to use on anything that contributes to your growth and development (agreed with your manager).
• Vacation/ Leave:
  • Holidays: 25 days of annual leave + bank holidays.
  • An extra day for your birthday.
  • Enhanced parental leave: 16 weeks fully-paid leave and leave.
• Private Health Insurance: We use Bupa!
• Full Access to Spill Mental Health Support
• Life Assurance: 4 times your salary to your beneficiaries.
• Cycle to Work Scheme

Diversity and Inclusion

We know Diversity and Inclusion is much deeper than just hiring, but it’s important for us to mention it here. We