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Agent guide: [/agents.md](https://www.wearedevelopers.com/agents.md). --- # Data Scientist - **Company:** Centre For Catholic Formation - **Location:** London, UK - **Experience:** Experienced - **Contract:** Permanent contract - **Skills:** A/B Testing, Artificial Intelligence, Microsoft Azure, Continuous Integration, Python (Programming Language), Machine Learning, Message Broker, Natural Language Processing, Standard Sql, Service-Oriented Architecture, Azure Service Bus, Large Language Models, Prompt Engineering, Model Validation, Agentic-AI, Codebase, Machine Learning Operations, Asynchronous Programming, Terraform, Docker, Databricks, Microservices - **Published:** October 15, 2026 - **Apply:** https://www.totaljobs.com/job/data-scientist/centre-for-catholic-formation-job108170915 ## About the Role 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 ## Description 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.