The Role
Join a Data & AI team building production LLM-powered agents for underwriting. Own end-to-end data science for production systems: prototype with stakeholders, design evaluation frameworks (offline and online), build analytical pipelines, measure agent quality, and work with engineers to productionise monitoring, CI/CD, and reliability.
Summary Generated by Built In
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.
About the role
- 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.
About you
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
Core Values
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.
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.
About
CFC is a specialist insurance provider, pioneering emerging risk and market leader in cyber. Our global insurance platform uses cutting-edge technology and data science to deliver smarter, faster underwriting and protect customers from today's most critical business risk.Headquartered in London with offices in New York, Melbourne, Sydney, Austin, Madrid, Brussels and Brisbane, CFC has over 1100 staff and is trusted by more than 100,000 businesses across 90 countries.At CFC, insurance isn't just about underwriting. From data science to software development, and digital marketing design, we've got something for everyone. We're passionate about pushing boundaries, thinking differently and building the insurance company of the future.CFC is committed to the principles of equal opportunities and creating an environment in which all individuals are always treated with dignity and respect. We encourage a diverse corporate culture of openness and appreciation to create an environment in which your talent can be developed in the best possible way. Should you require any reasonable adjustments at any stage of the recruitment process please let us know.
Skills Required
- Experience in Data Science, Applied Machine Learning, NLP or LLM-focused roles
- Strong Python skills and experience working in production codebases
- Strong SQL skills
- Hands-on experience evaluating, deploying and monitoring ML or LLM-powered applications
- Solid understanding of experimentation, model evaluation and A/B testing
- Experience with modern AI frameworks, agent architectures or RAG solutions
- Knowledge of cloud-based AI platforms, ideally within Azure
- Understanding of operationalisation, monitoring and maintenance of ML/AI systems in production
- Strong communication skills and ability to work with technical and non-technical stakeholders
- Ownership mindset and ability to work independently within cross-functional teams
- Prior experience in a business-critical / high-uptime production environment
- Experience with Databricks
- Understanding of asynchronous programming and containerised deployments (Docker)
- 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
CFC Compensation & Benefits Highlights
The following summarizes recurring compensation and benefits themes identified from responses generated by popular LLMs to common candidate questions about CFC and has not been reviewed or approved by CFC.
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Strong & Reliable Incentives — Variable pay is positioned as a core part of total compensation, with a group‑wide annual bonus highlighted as a consistent feature. Expanding employee share ownership is described as enhancing overall rewards alongside bonuses.
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Healthcare Strength — Private medical insurance is provided, complemented by dental and optical cashback and a 24/7 employee assistance programme. These elements indicate comprehensive health coverage beyond standard medical plans.
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Leave & Time Off Breadth — Time away provisions include 25 days of holiday and paid volunteer time, signaling a broad approach to time off. Additional practices such as company social events support overall work–life rhythm, though they are not leave per se.
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The Company
What We Do
CFC is a specialist insurance provider, pioneer in emerging risk and market leader in cyber. Their global insurance platform uses cutting-edge technology and data science to deliver smarter, faster underwriting and protect customers from today’s most critical business risks.
