Principal Data Scientist

Description

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.

London (hybrid, 3 days in office)

We are able to offer visa sponsorship for this position.

Data Science at Marshmallow

Our Data Science team partners across the business to turn data into better decisions, smarter products, and simpler customer journeys. We work closely with Product, Engineering, and Operations to build and ship models and AI systems that are reliable in production and deliver measurable impact.

Within Data Science, this role sits in Claims , supporting the function and the broader ambition to automate more of the claims journey. Claims is one of Marshmallow's most important customer touchpoints, and we're looking for a Principal Data Scientist who can provide technical leadership across traditional ML and Generative AI, bring system-level thinking to how we scale decisioning, and confidently challenge proposals to ensure we build robust, sustainable solutions.

What you'll be doing

Provide technical leadership for data science across Claims Fraud, shaping the approach to risk decisioning and fraud detection in partnership with Product and Engineering

Design, build and iterate on production ML and Generative AI/LLM systems that support claims validation and automation

Collaborate closely with other Claims data scientists to bring system-level thinking to how models, data and workflows fit together, identifying architectural improvements needed to scale decisioning and reduce time-to-production

Be vocal about the platform and tooling investments needed (monitoring, feedback loops, QA) to achieve AI-driven end to end claims automation

Advocate for robust, scalable, and strategically aligned technical solutions in cross-functional discussions, ensuring current systems and infrastructure contribute to the multi-year vision for automated claims handling

Set a high bar for statistical rigour, experimentation and measurement, helping improve how Claims performance and uncertainty are understood and communicated to senior stakeholders

Requirements

• You think in systems: you can connect the dots between data science, engineering, and product to shape scalable solutions that build on each other over time.
• You're confident in challenging assumptions and pushing for the right approach, using strong communication skills to influence stakeholders across seniority levels and disciplines with clear, pragmatic reasoning.
• You thrive in ambiguity and change, staying resilient and effective during transitions while bringing structure, clarity, and momentum to complex problem spaces.
• You're motivated by real-world impact, partnering closely with cross-functional teams to drive meaningful automation and better customer outcomes across the claims journey.
• What You'll Bring
• Significant commercial experience delivering end-to-end Machine Learning solutions, from problem framing and experimentation through to production deployment and ongoing monitoring
• Hands-on experience building and shipping Generative AI systems in production (not just prototypes), including evaluation, safety/quality considerations, and integration into customer or operational workflows
• Strong statistical and modelling foundation, with experience in risk-based decisioning under uncertainty (e.g., fraud, credit, insurance, or other regulated domains)
• Proven ability to influence technical direction across Data Science and Engineering, including shaping scalable model/service integration patterns and challenging proposals to drive robust, long-term solutions
• Strong stakeholder management skills, with confidence communicating trade-offs and pushing back constructively with Product and Engineering to ensure high-quality outcomes

Benefits

• Bonus scheme designed to reward high performance
• Private medical insurance with Vitality, mental health support with Oliva
• Personal learning budget and 2 dedicated L&D days a year
• Monthly flexible benefits budget to spend as you choose
• 25 days holiday plus bank holidays
• 4 weeks Work From Anywhere per year
• Our process
• Initial call with a member from our Talent Team (30 mins)
• Past Experience interview with Hiring Manager (60 mins)
• Systems Design & Technical interview with a couple of the team (90 mins)
• Culture interview (60 mins)