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 productionising 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 to make systems more robust and reliable.
- Prototype and iterate on features for AI/ML pipelines, taking ideas from early exploration through to measurable impact in production services.
- Document experiments, findings, and methodologies clearly so that insights are reproducible and decisions are traceable.
We're looking for a curious and technically strong Data Scientist who is passionate about applying AI and machine learning to complex, real-world business challenges. You'll be equally comfortable analysing data, designing experiments, engaging with stakeholders and collaborating with engineers to deliver production solutions.
You'll have:
- Experience working in Data Science, Applied Machine Learning, NLP or LLM-focused roles.
- Strong Python and SQL skills, with experience working in production codebases and collaborative engineering environments.
- Hands-on experience evaluating, deploying and monitoring machine learning or LLM-powered applications.
- A solid understanding of experimentation, model evaluation, A/B testing and performance measurement.
- Experience working with modern AI frameworks, agent architectures or retrieval-augmented generation (RAG) solutions.
- Knowledge of cloud-based AI platforms, ideally within Azure.
- An understanding of how AI and ML systems are operationalised, monitored and maintained in production.
- Strong communication skills and the ability to translate complex technical concepts into practical business outcomes.
- Confidence working directly with both technical and non-technical stakeholders to solve ambiguous problems.
- An ownership mindset, with the ability to work independently while contributing effectively within a cross-functional team.
Nice to have
- Prior experience in a business-critical / high-uptime production environment
- Experience with Databricks
- Understanding of asynchronous programming, containerised deployments (Docker), and modern service architectures
- Hands-on experience with Infrastructure as Code, particularly Terraform
- Experience designing and building distributed, asynchronous microservices using message brokers (e.g., Azure Service Bus, pub/sub).
- Knowledge of the insurance domain
Love what you do:
We show up each day ready to take on the world. Our passion and intensity set us apart and makes the difference to our colleagues, customers, brokers and carriers.
Challenge everything:
We’re never afraid to question the way that things are done and we constantly challenge ourselves and others to makes things better.
Have fun, be good:
Insurance is a serious business, but we don’t take ourselves too seriously. We make it fun to work at CFC, we welcome all viewpoints, and we treat everyone how we would expect to be treated.