Elliptic Data Science Lead at Elliptic leading a data science team focused on digital asset decisioning and compliance. Responsible for team growth, hiring, model governance, and building data foundations for compliance.
Responsibilities
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.
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.
Qualification
Work on agentic systemsBlockchain or on chain data experienceExperience in paymentsA PhD or equivalent research training
Preferred
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.
