Data Scientist

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

• 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.

• 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

Employment type

Permanent - Full Time