Data Science Lead

As Data Science Lead at Elliptic, you will lead a small data science team tackling an unsolved problem: how compliance systems work when the customer is an autonomous agent rather than a person. This is a player-coach role where you own team direction, performance and growth while remaining hands-on with the technical work yourself. You will represent your team's models at Elliptic's model risk governance forum, setting what the team works on, hiring the next people into it, and being the person whose technical judgement the team trusts because you can do the work.

What you'll do

• Lead and grow the team, setting objectives with your data scientists in the context of company strategy, owning their performance and development, and managing difficult conversations and levelling against Elliptic's Intelligence career framework
• Hire the next data scientists, owning the hiring process end to end including levels, job descriptions, hiring plan, interview panel and bar, and the hiring decision
• Build the collection for the future of compliance, taking it from directive to specification to delivered data, defining what good looks like, sequencing the work, setting the quality bar, and being honest about what the dataset does and does not support
• Own model governance for your team's models, keeping the model inventory current and correctly tiered, ensuring validation and testing evidence exists at the standard each tier requires, monitoring for drift and acting on threshold breaches, and submitting material changes and production deployments for review
• Lead Elliptic's data science work by framing the research question, running the first experiments, and forming a defensible view of what Elliptic needs to be able to do and by when, bringing that view to Intelligence leadership, the Chief Scientist and Product as a recommendation
• Stay technically hands on, writing code, reviewing your team's work at the level of method rather than output, and making the method calls yourself when they matter
• Own the team's existing commitments on clustering, attribution, heuristics and labelling that serve the platform, owning its reliability and trade-offs, and deciding what to stop
• Work across functions, partnering 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 unused analysis
• Set the AI working standard for the team, deciding how your team uses AI in its engineering and research workflow, what must be verified and how, and holding the team to it

What you bring

• AI fluency demonstrated through your ability to apply AI tools and approaches within a data science workflow (writing and debugging code, exploring unfamiliar data, accelerating pipeline and model work, automating repetitive tasks), and the ability to critically evaluate the output and set verification expectations for a team
• Demonstrated experience managing data scientists or machine learning engineers, including setting objectives, owning performance including underperformance, and developing people
• 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, with 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 Elliptic's, including 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
• A desire to lead a team without leaving the technical work behind, and honest clarity about where you draw that line
• Comfort being accountable for a question nobody has answered yet, including for saying what you do not know
• A view of management as the work rather than as overhead, including objectives, feedback, development and difficult conversations
• A view of governance as part of building good models rather than as paperwork that follows them, and a preference for surfacing a problem with your own model rather than having someone else find it
• Pragmatism about trade-offs between rigour and speed, with the ability to explain the trade-off you chose
• A drive to make other teams more effective rather than treating them as consumers of your team's output
• Curiosity about how the company operates beyond your own function
• A commitment to the mission of using intelligence to fight financial crime and protect the global financial system

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 such as 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 with the option to work from almost anywhere for up to 90 days per year
• Remote working budget of £500 to set up your home office space
• Learning and development budget of $1,000 per year to use on anything agreed with your manager that contributes to your growth and development
• 25 days of annual leave plus bank holidays, and an extra day for your birthday
• Private Health Insurance through Bupa
• Full access to Spill Mental Health Support
• Life Assurance cover of 4 times your salary to your beneficiaries
• Cycle to Work Scheme

About Elliptic

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