Associate Data Scientist

Associate Data Scientist

Department: Data & AI

Employment Type: Permanent
- Full Time

Location: UK
- London

Reporting To: Takashi Nakamura

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 AI to use.

CFC's Data & AI team is building production agentic systems that automate complex underwriting decisions — not demos, not proofs of concept sitting on a shelf, but Artificial Intelligence (AI) agents and machine learning (ML) pipelines that drive real business outcomes. This role will sit alongside senior data scientists, machine learning engineers, and software engineers, as an analytical counterpart: running experiments, stress-testing assumptions, and generating the evidence that shapes what gets built and how to improve it over time.

This is an Associate Data Scientist role designed for someone early in their career who wants genuine exposure to production AI systems from day one. You'll work closely with, and have the opportunity to learn from, team members while contributing analysis and experimentation work that feeds into live services — such as statistical analysis and LLM evaluation. This will require the kind of careful, evidence-based thinking that makes the difference between an AI system that works in a notebook and one that holds up under real conditions.

The problems are genuinely hard. The data is complex, the decisions are high-stakes, and the domain has the kind of depth that keeps the work interesting. If you want to grow quickly in applied AI and ML — working on real systems, not toy datasets — this is the right environment.

• Design and run experiments that directly shape production AI agents — testing ideas, validating approaches, and turning research into deployed improvements.
• 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.
• Evaluate and refine LLM-powered workflows — building robust evaluation frameworks, stress-testing agent behaviour, and driving continuous quality improvements in live systems.
• Explore complex, real-world datasets to uncover insights that meaningfully improve underwriting decisions and system performance at scale.
• Prototype and iterate on features for AI/ML pipelines, taking ideas from early exploration through to measurable impact in production services.
• Investigate how agentic systems behave in production — identifying opportunities for improvement, failure modes, and ways to make systems more robust and reliable.
• Collaborate closely with ML/AI engineers to bridge research and production — contributing code, debugging issues, and shipping improvements end-to-end.
• Document and present experiments, findings, and methodologies clearly to both technical and business users, making sure insights are reproducible and decisions are traceable.

You will have experience/be familiar with the following:
AI and LLMs

• Familiarity with LLM-powered apps and services, evaluation frameworks, prompt engineering, and other generative AI work — whether through professional experience, coursework, or personal projects.
• Curiosity about how LLM-based systems behave in production and how to evaluate them rigorously.
• Experience with ML models (including deep models) and their lifecycle, including training, evaluation and inference.
• Exposure to experiment tracking tools (e.g. MLflow).

• Proficiency in Python.
• Working knowledge of SQL for querying and preparing data.
• Comfort with Git and version control as part of a collaborative workflow.
• Basic grounding in CI/CD concepts and an interest in how software is tested and deployed.

• Solid grounding in statistics and probability — comfortable with hypothesis testing, distributions, and drawing defensible conclusions from data.
• Hands-on experience with exploratory data analysis, feature engineering, and building and evaluating ML models.
• Working knowledge of the core ML model lifecycle: data…