At a Glance
- Tasks: Lead data science initiatives to enhance claims automation and fraud detection.
- 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 at Marshmallow.
- Why this job: Make a real-world impact by driving meaningful automation in the claims journey.
- Qualifications: Experience in machine learning and generative AI systems is essential.
The predicted salary is between 70000 - 90000 £ 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 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 Staff 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. Plus 2 days a year - on us! - to further your skillset.
- 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.
We are able to offer visa sponsorship 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 & Technical interview with a couple of the team (90 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.
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.
Staff Data Scientist in London employer: Marshmallow
At Marshmallow, we pride ourselves on being an excellent employer, offering a dynamic and supportive work culture that fosters innovation and collaboration. As a Senior Android Engineer in London, you'll benefit from our commitment to employee growth through mentorship and continuous learning opportunities, all while contributing to meaningful projects that empower migrants in the UK. Join us to be part of a team that values your expertise and encourages you to take ownership of your work in a hybrid environment.
StudySmarter Expert Advice🤫
We think this is how you could land Staff Data Scientist in London
✨Get Involved in Data Science Meetups
Tap into local data science meetups or workshops to connect with fellow enthusiasts and professionals. These events are goldmines for networking, and sometimes even lead directly to job openings at companies like Marshmallow!
✨Show Off Your Projects
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✨Leverage Professional Networks
Join professional bodies related to data science, like the Data Science Society or similar organisations. Getting involved can lead to mentorship opportunities and insider knowledge about full-time positions at companies like Marshmallow.
✨Apply Directly through Our Website
When you find a suitable opening like Staff Data Scientist at Marshmallow, make sure to apply directly through our website. It gives you an edge and shows you're keen to join our team. Plus, who doesn’t love a direct application? It’s easier than navigating through job boards!
We think you need these skills to ace Staff Data Scientist in London
Some tips for your application 🫡
Show Off Your Projects:In the world of data science, your projects can speak volumes about your skills. Make sure to showcase a few key projects in your CV or portfolio, especially those that highlight your ability to work with data sets, build models, or use relevant tools like Python, R, or SQL. Don’t forget to include links to any GitHub repositories if applicable!
Quantify Your Achievements:Employers love numbers! When drafting your CV, highlight your achievements with quantifiable results. For instance, mention how your data analysis led to a certain percentage increase in efficiency or revenue at a previous job or project. These details can really make your application pop!
Craft a Tailored Cover Letter:For a full-time role at Marshmallow, your cover letter should reflect your passion for data science and your excitement about the specific projects or values of the company. Dive into why you’re a good fit, how your skills align with their needs, and any unique perspectives you can bring to the team.
Stand Out with Relevant Courses and Certifications:Although experience talks, relevant courses or certifications can be your ticket to impressing hiring managers at Marshmallow. Mention any standout courses you've completed that equipped you with essential skills, such as machine learning certifications or data visualisation courses. This shows your commitment to continuously developing your skills in the field!
How to prepare for a job interview at Marshmallow
✨Brush Up on Your Statistics
For a data science role, we need to seriously sharpen our statistics skills. Get ready to tackle technical questions on probability distributions, hypothesis testing, and regression analysis. These are often the bread and butter of data science interviews, so don't just skim over them!
✨Showcase Your Projects
Prepare a killer portfolio showcasing your data science projects. We should include details about the datasets used, the tools and techniques applied, and the impact of your findings. If we can walk them through a particularly challenging project or a cool visualisation that had real-world implications, it’ll really make us stand out!
✨Get Comfortable with Python and R
Most data science positions require us to be proficient in programming languages like Python and R. We should practice common libraries like pandas, NumPy, and scikit-learn, and be ready for live coding exercises or algorithm questions. Showing off our coding chops can really impress the interviewers at Marshmallow!
✨Prepare for Case Studies
Expect to encounter real-world case studies during the interview. We might be asked how we’d approach a data problem or analyse a dataset to extract insights. It's essential to think out loud and demonstrate our problem-solving process so that the interviewer can see our logical thinking in action.