Data Scientist

Insurance isn’t the first industry most data scientists think of when they imagine cutting-edge Artificial Intelligence (AI) work, but the incredibly rich data and nature of the business make it a great place to put cutting-edge AI to use.

Department:
Data & AI

Location:

UK - London

Insurance isn’t the first industry most data scientists think of when they imagine cutting-edge Artificial Intelligence (AI) work, but the incredibly rich data and nature of the business make it a great place to put cutting-edge AI to use.

CFC's Data & AI team is building production agentic and ML systems that automate and inform complex underwriting decisions that drive real business outcomes - not demos, not proof-of-concepts sitting on a shelf. The team includes ML engineers and software engineers shipping production services, and this role sits alongside them as an analytical counterpart: running experiments, stress-testing assumptions, and generating the evidence that shapes what gets built and how it improves over time.

We are looking for a mid-level Data Scientist to join the team that owns business-critical, live solutions utilising Large Language Models (LLMs), such as an email ingestion/extraction solution and underwriting agents. This is not a pure research or offline-modelling role - when research is carried out and potential opportunities identified it is expected that you will work closely with ML engineers and software engineers to build this into a live system, where quality, reliability, and evaluation rigor directly affects the business.

We expect that a successful candidate will be able to own the data science side of a production LLM system end-to-end: partnering with stakeholders to build early prototypes, designing evaluation frameworks, measuring agent quality, and turning ambiguous "is this good?" questions into repeatable, defensible metrics - while working closely with engineers to understand what it takes to take that work from prototype to live system.

• Explore complex, high dimensional, real-world datasets to uncover insights that meaningfully improve underwriting decisions and system performance at scale.
• Partner directly with underwriting and business stakeholders to scope problems, assess feasibility, and build early prototypes (e.g. PoC agents, rapid evaluation of an LLM approach) before committing engineering investment.
• Stay involved from prototype through to production, working with ML/software engineers to harden, scale, and maintain what you've built as a key contributor to the codebase.
• Design and run evaluation frameworks for LLM-powered agent behaviour, including offline (golden datasets, regression suites) and online (production monitoring, A/B testing) evaluation.
• Build and maintain analytical pipelines — prompt design, calibration against human labels, bias/consistency checks, LLM-as-a-judge, and ongoing validation that the judge stays trustworthy as the underlying models change.
• Partner with ML engineers to design system nodes/components, translating data science findings into concrete engineering requirements.
• Define quality metrics for agent outputs (accuracy, hallucination rate, task completion, groundedness, latency/cost trade-offs) and track them over time.
• Work with software engineers on product ionising evaluation and monitoring code: CI/CD integration, release gating, and operational readiness (alerting, dashboards, on-call awareness).
• Actively explore cutting-edge developments in AI and machine learning — with the space and support to experiment, prototype, and bring new techniques into production where they add value.
• Investigate how agentic systems behave in production — identifying edge cases, failure modes, and opportunities…