At a Glance
- Tasks: Lead data science initiatives to enhance claims fraud detection and automation.
- Company: Join Marshmallow, a forward-thinking company making migration easier.
- Benefits: Enjoy flexible working, competitive bonuses, and a personal benefits budget.
- Other info: Embrace a culture of continuous growth and inclusivity.
- Why this job: Make a real-world impact by driving automation in the claims journey.
- Qualifications: Experience in machine learning and generative AI systems is essential.
The predicted salary is between 43200 - 72000 £ per year.
About Marshmallow
We exist to make migration easy. A systemic problem of this magnitude requires a team of curious thinkers who relentlessly pursue solutions. Those who constantly challenge the why, dismantle assumptions, and always take action to build a better way. A Marshmallow career is built on a cycle of continuous growth, with learning at its core. You will be challenged to raise the bar on your capabilities and supported with the right tools and guidance to do so. This ensures you can deliver impactful work and drive change.
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 Claims Fraud 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.
Who You Are
- 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.
Perks of the job
- Flexible working: Spend three days a week with your team in our collaborative London office, and own your own working hours.
- Competitive bonus scheme - designed to reward and recognise high performance.
- Flexible benefits budget - £50 per month to spend on a Ben Mastercard meaning you get your own benefits budget to spend on things you want.
- Mental wellbeing support – Access therapy and mental health sessions through Oliva.
- Learning and development – Personal budgets for books and training courses to help you grow in your role.
- Private health care - Enjoy all the benefits Vitality has to offer, including reduced gym memberships and discounts on smartwatches.
- Medical cash plan - To help you with the costs of dental, optical and physio (plus more!).
- Tech scheme - Get the latest tech for less.
- Plus all the rest; 33 days holiday (including bank holidays), pension, cycle to work scheme, monthly team socials and company-wide socials every month!
We are able to offer sponsorship and/or a visa for this position.
Our process
- Initial call with a member from our Talent Team (30 mins).
- Past Experience interview with Hiring Manager (60 mins).
- Systems Design interview with a couple of the team (60 mins).
- Technical interview with a couple of the team (60 mins).
- Culture interview (60 mins).
- Background checks.
As part of our commitment to maintaining a safe and trustworthy environment, we’ll carry out standard background checks, including a DBS and a Cifas check. These help ensure there are no ongoing criminal proceedings and support the prevention of fraud and other forms of serious misconduct. If anything of concern is identified, it may affect your eligibility for certain roles or services.
Everyone belongs at Marshmallow
At Marshmallow, we want to hire people from all walks of life with the passion and skills needed to help us achieve our company mission. To do that, we’re committed to hiring without judgement, prejudice or bias. We encourage everyone to apply for our open roles. Gender identity, race, ethnicity, sexual orientation, age or background does not affect how we process job applications. We’re working hard to build an inclusive culture that empowers our people to do their best work, have fun and feel that they belong.
Recruitment privacy policy
We take privacy seriously here at Marshmallow. Our Recruitment privacy notice explains how we process and handle your personal data.
Principal Data Scientist employer: Marshmallow
Contact Detail:
Marshmallow Recruiting Team
StudySmarter Expert Advice 🤫
We think this is how you could land Principal Data Scientist
✨Tip Number 1
Network like a pro! Reach out to current employees at Marshmallow on LinkedIn or other platforms. Ask them about their experiences and any tips they might have for the interview process. This insider info can give you a leg up!
✨Tip Number 2
Prepare for those interviews by brushing up on your technical skills. Since you're aiming for a Principal Data Scientist role, be ready to discuss your experience with ML and Generative AI in detail. Practice explaining complex concepts in simple terms – it shows you can communicate effectively!
✨Tip Number 3
Showcase your problem-solving skills during interviews. Marshmallow loves curious thinkers who challenge assumptions. Bring examples of how you've tackled tough problems in the past and what impact your solutions had.
✨Tip Number 4
Don’t forget to apply through our website! It’s the best way to ensure your application gets seen. Plus, it shows you’re genuinely interested in being part of the Marshmallow team. Good luck!
We think you need these skills to ace Principal Data Scientist
Some tips for your application 🫡
Tailor Your Application: Make sure to customise your CV and cover letter for the Principal Data Scientist role. Highlight your experience with Machine Learning and Generative AI, and show how your skills align with Marshmallow's mission to automate claims processes.
Showcase Your Impact: When detailing your past experiences, focus on the real-world impact of your work. Use specific examples that demonstrate how you've driven meaningful change in previous roles, especially in data science and risk decisioning.
Be Clear and Concise: Keep your application clear and to the point. Use straightforward language to explain complex concepts, as this will reflect your ability to communicate effectively with cross-functional teams at Marshmallow.
Apply Through Our Website: We encourage you to apply directly through our website. This ensures your application gets to the right people and helps us keep track of all candidates efficiently. Plus, it’s super easy!
How to prepare for a job interview at Marshmallow
✨Understand the Business Context
Before your interview, dive deep into Marshmallow's mission and values. Familiarise yourself with how data science impacts their claims process and customer journeys. This will help you articulate how your experience aligns with their goals and demonstrate your genuine interest in the role.
✨Showcase Your Technical Leadership
Prepare to discuss specific examples where you've provided technical leadership in data science projects. Highlight your experience with machine learning and generative AI systems, focusing on how you've influenced decisions and shaped scalable solutions. Be ready to challenge assumptions and present your reasoning clearly.
✨Collaborate and Communicate
Since this role involves working closely with cross-functional teams, practice articulating your ideas and technical concepts in a way that non-technical stakeholders can understand. Use examples from your past experiences to illustrate how you've successfully collaborated with product and engineering teams.
✨Prepare for System-Level Thinking
Brush up on system-level thinking and be prepared to discuss how different components of data science, engineering, and product fit together. Think about architectural improvements you've implemented in previous roles and how they contributed to scaling decision-making processes. This will show your ability to think holistically about the challenges at Marshmallow.