Level

Marshmallow

Head of Data Science (12m fixed-term contract)

AI in this role

claudelangchainmlflowsagemakercursorclaude-codesierra

We’re on a mission to make migration easy.

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 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 and best practice.

Who you are

  • You bring an inherently commercial-first mindset: you don’t just build models, you build solutions that directly influence the P&L, and you constantly evaluate every technical trade-off through a rigorous lens of business value.

  • You make confident decisions even when the picture is still forming, and you're comfortable operating across more than one domain at once.

  • You have a track record of driving visible improvements to team culture, ways of working, and technical standards.

  • You build trust quickly with a team, and coach others to challenge and disagree constructively, to drive decisions that deliver the highest-impact outcomes

  • You thrive in a fast paced and ever-evolving environment; you adapt to change well (and in fact relish it!).

  • You like to take real ownership of your work, drive yourself forward, and work well both autonomously but also collaboratively with the team and your stakeholders.

What we’re looking for from you

Must have:

  • Excellent leadership and team management skills.

  • A strong background as a senior data science individual contributor, with commercial experience delivering ML solutions end to end

  • 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 to C-suite and senior stakeholders across different functions to ensure high-quality outcomes.

Nice to have:

  • Experience in insurance, fraud, or another regulated decisioning domain.

  • Experience of contributing towards the strategy for Gen AI within an organisation, and leading data scientists to deliver Gen AI solutions that drove measurable business value

(We cannot offer visa sponsorship for this role)

Perks & 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

  1. Recruiter call with the Talent Acquisition team.

  2. Past experience interview with Hannah Penfold, Hiring Manager.

  3. Technical deep dive or case study focused on technical trade-offs, commercial thinking and influence (2 Senior Stakeholders)

  4. Final leadership and values interviews with senior technology and commercial stakeholders, (CTO & Co-Founder)

We review every application and will always let you know the outcome, though we're unable to provide individual feedback at application stage.

Diversity of thought

We know the best ideas come from having different perspectives in the room - and we're committed to hiring fairly, regardless of background, identity or experience. If you see yourself in this role, we'd encourage you to apply.

How we rate this

Head of Data Science (12m fixed-term contract) at Marshmallow rates 28 out of 100 for how much of the daily work is AI. That makes it Little AI (AI Level 1 of 4). The level is about AI in the job, not seniority.

Classification

Little AI. AI is not part of the work.

  1. ●●●● Builds AI80 to 100
  2. ●●●○ Works on AI60 to 79
  3. ●●○○ Uses AI40 to 59
  4. ●○○○ Little AI0 to 39

Levels come from how often the tools, models and workflows of the role are named in the posting itself. Open the description and count.

Prepare for this job

A free preview built only from this posting: what it asks for, what you could be asked in an interview, and how to adjust your resume.

Skills and AI tools this role asks for

ClaudeLangChainMlflowSagemakerCursorClaude CodeSierra

Questions you could be asked

  1. What's a project where you used Claude hands-on?
  2. Walk me through how you've used LangChain in your day-to-day work.
  3. What are the limits of Mlflow that you've run into, and how did you work around them?
  4. What's a project where you used Sagemaker hands-on?
  5. Walk me through how you've used Cursor in your day-to-day work.

Adapt your resume

  • List these exact terms on your resume: Claude, LangChain, Mlflow, Sagemaker, and Cursor. An applicant tracking system matches the wording, not the idea.
  • Attach one line of real, concrete experience to at least one of them — a tool named with nothing behind it rarely survives a human read.

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