CFC

Cyber risk underwriting through MGA platform

Data Scientist

Natural Language Processing & LLMsUpdated on Nov 13

Full-timeLondon, UKNo salary listedHybrid

About the Job

Requirements

Experience working in data science, applied machine learning, natural language processing, or large language model-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 large language model-powered applications.
A solid understanding of experimentation, model evaluation, A/B testing, and performance measurement.
Experience working with modern artificial intelligence frameworks, agent architectures, or retrieval-augmented generation solutions.
Knowledge of cloud-based artificial intelligence platforms, ideally within Azure.
An understanding of how artificial intelligence and machine learning systems are operationalized, monitored, and maintained in production.
Strong communication skills and the ability to translate complex technical concepts into practical business outcomes.
Confidence working directly with 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.

Responsibilities

Explore complex, high-dimensional, real-world datasets to uncover insights that improve underwriting decisions and system performance at scale.
Partner directly with underwriting and business stakeholders to scope problems, assess feasibility, and build early prototypes, such as proof-of-concept agents or rapid evaluations of a large language model approach, before committing engineering investment.
Stay involved from prototype through production, working with machine learning and software engineers to harden, scale, and maintain solutions as a key contributor to the codebase.
Design and run evaluation frameworks for large language model-powered agent behavior, including offline evaluation with golden datasets and regression suites, and online evaluation with production monitoring and A/B testing.
Build and maintain analytical pipelines for prompt design, calibration against human labels, bias and consistency checks, large language model-as-a-judge evaluation, and ongoing validation of the judge as underlying models change.
Partner with machine learning engineers to design system nodes and components, translating data science findings into concrete engineering requirements.
Define quality metrics for agent outputs, including accuracy, hallucination rate, task completion, groundedness, and latency/cost trade-offs, and track them over time.
Work with software engineers on productionizing evaluation and monitoring code, including continuous integration and deployment integration, release gating, and operational readiness such as alerting, dashboards, and on-call awareness.
Explore developments in artificial intelligence and machine learning, experiment and prototype, and bring useful techniques into production.
Investigate how agentic systems behave in production by identifying edge cases, failure modes, and opportunities to improve robustness and reliability.
Prototype and iterate on features for artificial intelligence and machine learning pipelines, taking ideas from early exploration through to measurable impact in production services.
Document experiments, findings, and methodologies so insights are reproducible and decisions are traceable.

Desired Qualifications

Prior experience in a business-critical or high-uptime production environment.
Experience with Databricks.
Understanding of asynchronous programming, containerized deployments using Docker, and modern service architectures.
Hands-on experience with Infrastructure as Code, particularly Terraform.
Experience designing and building distributed, asynchronous microservices using message brokers such as Azure Service Bus or publish/subscribe systems.
Knowledge of the insurance domain.

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

About the Company

CFC

Cyber risk underwriting through MGA platform

CFC Underwriting is a specialist insurance provider operating as a managing general agent (MGA) that underwrites on behalf of a panel of carriers, focusing on emerging and specialty risks with a strong emphasis on cyber. It uses a proprietary global insurance platform powered by data science to streamline underwriting across a portfolio of 50+ products, including cyber and professional liability, with an in-house incident response team adding comprehensive cyber risk management. It differentiates itself through its early focus on cyber, large in-house claims and incident response capabilities, and a global broker-driven distribution model. Its goal is to deliver fast, precise underwriting and robust risk management for technology-related and other specialty risks worldwide.

Company size: 1,001-5,000
Company stage: Not listed
Total funding: Not listed
Headquarters: London, United Kingdom
Founded: 1999

Recently Posted Jobs

Explore more opportunities at CFC

See more jobs