Posted 1mo ago
Pay
Not stated
Location
London
On site
Licensed visa sponsor (Home Office register, 26 Oct 2026)
Listed as Marshmallow Technology Ltd, London, licensed for the Skilled Worker route. A licence means the employer can sponsor a visa; whether it sponsors a given role is up to the employer.
Official sourceJobs at licensed visa sponsors in the United Kingdom
Skills this role screens for
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Still studying?Python internships in the United Kingdom open nowResponsibilities
Requirements
We started building Marshmallow in 2017. Since then, we’ve grown from 3 to 700+ people, gained unicorn status, raised ~£140M over three funding rounds, turned profitable, insured millions of drivers and lent millions in car loans. But we’re only just getting started. Our goal is to become one of the largest financial services providers in the world. Over the next 10 years we’ll grow exponentially, not only by scaling our existing products, but also by building new ones. To achieve our goals we need incredibly ambitious, commercially driven people who never settle for ‘good enough’. Marshmallowers are hungry for autonomy and ownership, and would rather improve than coast. Everyone raises standards and has an impact, with a focus on collective success over self-interest. We’ve created an environment where curious, tenacious people win and grow together. If that sounds motivating, this could be the place for you. 12m FTC - (London, hybrid 3 days a week) DATA SCIENCE AT MARSHMALLOW Our data science team supports various business functions across our Product teams and within Pricing and Claims. Working across the full spectrum of data science problems, from traditional machine learning for risk-based decisioning through to generative AI and the automation of complex operational journeys, all within a regulated insurtech/fintech domain. This is a hands-on leadership role for someone who can bring a team together and ensure it delivers at pace while its foundations strengthen: leading and developing the people, personally holding the bar on technical quality, and partnering closely with engineering and product. It suits a leader who is strong both leading and growing teams as well as a technical decision-maker. ABOUT THE TEAM The Data Science team operates as a primary commercial engine for Marshmallow, using data and machine learning to drive tangible business outcomes, from pricing strategy to operational efficiency. The team works closely with senior stakeholders and partners across data, engineering, product and commercial functions to identify the right opportunities and make thoughtful technical trade-offs. We operate with a flat structure, so leaders stay close to the people doing the work and support colleagues at different levels of experience. Our ambition is to keep raising the bar for how data science delivers impact across the business. PRODUCT & TECH AT MARSHMALLOW Backend and Infrastructure: Latest Java μService, Spring Boot with Spring Cloud, Dynamodb, Terraform, Docker, AWS Fargate, Datadog, Opslevel, and TeamCity. Frontend and Mobile: TypeScript, CSS-in-JS (Styled Components), React, Redux, Redux Hooks, and other modern state management libraries. Kotlin on Android and Swift on iOS. Data: SQL, Python, Snowflake, dbt, Airflow & Looker AI at Marshmallow: Sierra.ai http://Sierra.ai for conversational AI, Python and LangChain for agentic AI solutions, Sagemaker and MLflow for Machine Learning, Cursor and Claude Code for developer productivity. WHAT YOU'LL BE DOING - Drive commercial impact at every turn: Proactively identify and prioritise data science opportunities that offer the highest leverage for the business, ensuring every project is measured by its bottom-line contribution and strategic commercial alignment. - Lead the people and culture agenda for data science: set goals, manage performance and development conversations, and keep an evolving team engaged and delivering high-impact projects. - Provide hands-on technical leadership: set and hold a consistent bar on technical standards across the full modelling and data lifecycle. - Represent data science in cross-functional conversations, partnering closely with engineering, product and pricing on technical direction and trade-offs. - Build collective ownership of technical standards across Data Science, coaching people to look beyond their own domain and drive function-wide improvements to shared standards, tooling an
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