Applied Machine Learning Engineer

Who We Are

Since its inception, [Employer hidden — sign up to reveal] Group has sought a different approach to insurance. We are on a mission to be the ‘most inspiring specialty (re)insurance group in the world’. At the heart of [Employer hidden — sign up to reveal] are our people. Working together in dynamic, service-focused teams, we prioritise our customers in everything we do. Collaboration fuels our success, courage drives our innovation and continuous improvement keeps us ahead in a rapidly evolving industry. Our shared commitment is to revolutionise insurance for the better, one day at a time. We also believe that investing in our people is investing in our future. By empowering people across the Group to develop their careers, advance within the Group, and embrace new challenges, we build an environment where growth and learning never stop. Our competitive benefits package, offered via a flexible benefits platform, reflects this. Beyond core benefits, employees have the freedom to tailor their benefits to meet their individual needs, supporting their unique goals and ambitions.

Role Summary

Applied Machine Learning Engineer (Underwriting Decision Modelling Focus) builds the data backbone that powers our machine learning ecosystem from behavioural telemetry and event pipelines to feature stores and complex data transformations. You’ll ensure ML teams have timely, reliable, and well‑structured data for both model training and real‑time production inference. You’ll work end‑to‑end across the entire insurance value chain, shaping how data flows into underwriting workflows, pricing engines, document intelligence systems, decisioning models, and claims automation. As a key connector across the business, you’ll partner with actuaries, underwriters, ML engineers, data scientists, and product teams to create the enterprise‑wide machine learning fabric. Your work will directly enable underwriting and pricing teams around the world driving automation, smarter decision‑making, improved accuracy, and operational efficiency at a global scale. Our digital suite is a governed decisioning system that unifies insurance from capacity to close, turning documents and data into evidence and converting rules and models into consistent, explainable, auditable outcomes. Built to replace outdated, disconnected systems, gives ambitious insurance brands a fully integrated ecosystem that removes silos and delivers the speed, clarity, and control teams need to build what’s next.

Key Responsibilities

• Data & Telemetry Engineering: Design, instrument, and maintain event-driven behavioural telemetry across underwriting, quoting, rating, and workflow systems. Capture user interactions, model outputs, decision pathways, and operational signals required for ML models and analytics.
• Pipeline & Feature Engineering: Build large-scale batch and streaming data pipelines with strong reliability, validation, and lineage. Develop and manage the enterprise feature store ensuring consistent and reproducible features across training, validation, and real-time inference. Partner with ML scientists to convert raw data into ML-ready features, labels, and training datasets.
• Data Architecture & Governance: Define and maintain data schemas and ontologies, aligned with ACORD-style standards and organisational capability matrices. Implement robust data governance, including metadata management, quality enforcement, and auditing. Support historical replay, back testing, and offline simulation environments for underwriting and pricing models.
• ML Productionisation: Collaborate with ML engineers to deploy, monitor, and scale models within underwriting, decisioning, and document intelligence workflows. Ensure ML pipelines meet requirements for latency, explainability, reproducibility, and compliance.

Role Requirements

• Strong hands-on experience with Python, SQL, and NoSQL data stores.
• Expertise in distributed data processing (Spark, Beam, Databricks, Flink, or equivalent).
• In-depth understanding of modern ETL/ELT patterns and orchestration tools (Airflow, dbt, Azure Data Factory, AWS Glue).
• Experience with real-time data and streaming frameworks such as Kafka, Kinesis, EventHub, or similar.
• Familiarity with implementing low-latency feature pipelines and online feature computation.
• Experience working with or building feature stores (Feast, Hopsworks, Databricks Feature Store, etc.).
• Knowledge of ML workflow tools and data versioning (MLflow, Tecton, DVC, Delta Lake, Lakehouse).
• Strong knowledge of data modelling, entity definitions, metadata frameworks, and lineage tracking.
• Ability to design schemas suitable for underwriting, rating, claims, and actuarial contexts.
• Proficiency with cloud-native services (Azure, AWS, or GCP).
• Understanding of data warehouse and lakehouse architectures (Snowflake, BigQuery, Redshift, Databricks).
• Exposure to BI/MI tooling (Power BI, Tableau) is beneficial.

Additional Information

We are an equal opportunity employer, and we are proud to share that 93% of our employees say they can be themselves at work. We aim to hire our industry's finest people because the best people drive the best outcomes. And we forever challenge the status quo because we know there are always ways to improve things. Because together, we're limitless. We value applicants from all backgrounds and foster a culture of inclusivity. We understand the need for flexibility, so work in a hybrid model. Please let us know if you require any reasonable adjustments during the recruitment process.

FCA Conduct Rules Under the Senior Managers and Certification Regime the FCA and [Employer hidden — sign up to reveal] expects that: 1. You must act with integrity. 2. You must act with due skill, care and diligence. 3. You must be open and cooperative with the FCA, the PRA and other regulators. 4. You must pay due regard to the interests of customers and treat them fairly. 5. You must observe proper standards of market conduct. 6. You must act to deliver good outcomes for retail customers.