Senior Data Scientist

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. LONDON (HYBRID, 3 DAYS IN OFFICE)   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 Senior Data Scientist who can provide technical expertise 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 * Build and iterate on multimodal AI models that reduce claims cost and improve claims processing, including models that analyse emails, documents, and claim summaries for operational teams * Develop machine learning models that support claims automation, including use cases such as negotiation strategies, litigation strategies, and total loss prediction * Explore and evaluate new data sources that could improve model performance and decision-making, such as fraud signals, open banking, and telematics data * Design and build agentic AI solutions to automate and streamline claims workflows * Collaborate closely with Product, Data, and Engineering teams to test hypotheses, develop new features, and turn ideas into production-ready solutions * Work with the MLOps team to improve data science and AI model infrastructure, including deployment, monitoring, evaluation, and feedback loops * Help define the right technical approach for problems, balancing speed, quality, and scalability while ensuring solutions are practical for the business * Set a strong standard for experimentation, measurement, and model performance, helping the team understand impact, uncertainty, and trade-offs clearly 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 * Strong 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 production AI or machine learning systems, including evaluation, quality considerations, and integration into operational workflows * Experience working on applied problems involving structured and unstructured data, with an interest in multimodal modelling and AI systems * A strong statistical and modelling foundation, with experience working on risk-based decisioning or other complex, uncertain problem domains * Proven ability to work cross-functionally with Product, Engineering, Operations, and MLOps to deliver scalable solutions * Strong communication and stakeholder management skills, with confidence in discussing trade-offs and pushing back constructively when needed   PERKS OF THE JOB * 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   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) * Technical interview with a couple of the team (90 mins) * Culture interview (60 mins) 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.

Multiverse is the upskilling platform for AI and Tech adoption. We have partnered with 1,500+ companies to deliver a new kind of learning that's transforming today’s workforce. Our upskilling apprenticeships are designed for people of any age and career stage to build critical AI, data, and tech skills. Our learners have driven $2bn+ ROI for their employers, using the skills they’ve learned to improve productivity and measurable performance. In April 2026, we announced $70 million in strategic funding, led by Schroders Capital, with participation from StepStone Group, Lightspeed Venture Partners and General Catalyst. At an increased valuation of $2.1bn, the round makes us Europe’s first EdTech double unicorn. But we aren’t stopping there. With a strong operational footprint and 800+ employees, we have ambitious plans to continue scaling. We’re building a world where tech skills unlock people’s potential and output. Join Multiverse and power our mission to equip the workforce to win in the AI era. WHAT WE NEED: At Multiverse, the models we build don't just sit in notebooks - they drive the decisions that shape our business every day. From predicting learner outcomes to forecasting operational demand and optimising how we allocate resources, this work sits at the very core of how we run the company. As a Senior Data Scientist, you'll own these models end to end. You'll develop a deep understanding of how Multiverse operates across our customer, learner and operational domains - and translate that understanding into rigorous, production-grade ML models that genuinely move the needle. To be successful, you'll be comfortable getting hands-on with pipelines and infrastructure - and unafraid of the statistical rigour that serious modelling demands. You'll work closely with stakeholders across every part of the business - helping them ask better questions, understand the answers, and act on them with confidence. Our leaders will make multi-million dollar decisions based on your recommendations, and our AI-powered product will decide how to support learners based on your models. You'll sit within our Data & Insight team, working day-to-day alongside Data Engineers, Data Product Developers and Insight Analysts. WHAT YOU'LL FOCUS ON: Business Understanding & Problem Definition * Building genuine expertise in how Multiverse operates across customer, learner and operational domains - becoming a trusted thought partner * Translating complex and often ambiguous business questions into well-scoped modelling problems with clear success criteria * Identifying where predictive, forecasting or optimisation models can have the greatest business impact, and prioritising accordingly Modelling & Statistical Analysis * Designing, developing and iterating supervised and unsupervised ML models that predict, forecast and optimise across the business * Applying rigorous statistical methods to ensure models are robust, unbiased and genuinely causal wherever causal claims are being made * Developing a deep understanding of our data landscape - its lineage, quirks, and limitations - and designing approaches that account for them * Monitoring and refining models over time, ensuring they remain accurate and relevant as the business evolves Data Engineering & Infrastructure * Collaborating closely with Data Engineers to build and maintain the data pipelines and ML infrastructure needed to develop and deploy your models * Productionising models to run reliably at scale, adhering to software engineering best practices - including version control, CI/CD and vulnerability management * Evaluating and implementing scalable approaches to data collection and processing, ensuring robust practices are in place WHAT WE'RE LOOKING FOR: Required * 5+ years of data science/machine learning experience, with a proven track record building and deploying models that drive real business decisions * Deep expertise in predictive modelling, forecasting and/or optimisation - with strong command of the underlying statistical principles * Strong proficiency in Python and core ML libraries (e.g., NumPy, Pandas, Scikit-Learn, xgboost, shap) * Advanced working knowledge of SQL * Hands-on experience with data pipelines and ML infrastructure * Experience working within AWS (ideally using Sagemaker) and/or Azure * Comfort working across our data stack - inc Airflow, Snowflake * Experience with version control and CI/CD practices (ideally using GitHub) * Rigorous attention to statistical validity - comfortable challenging assumptions and defending methodology * Understanding of best practices in data protection and information security Desirable * Experience with causal inference methods (e.g., diff-in-diff, instrumental variables, propensity score matching) * Experience with dbt for data transformation * Knowledge of infrastructure as code tools (e.g. Terraform) * Strong professional and/or academic background within a highly quantitative discipline (e.g. statistics, mathematics, physics or economics) Benefits * Time off - 27 days holiday, plus 5 additional days off: 1 life event day, 2 volunteer days, 2 company-wide wellbeing days (M-Powered Weekend) and 8 bank holidays per year * Health & Wellness- private medical Insurance with Bupa, a medical cashback scheme, life insurance, gym membership & wellness resources through Wellhub and access to Spill - all in one mental health support * Hybrid work offering - for most roles we collaborate in the office three days per week with the exception of Coaches and Instructors who collaborate in the office once a month * Work-from-anywhere scheme - you'll have the opportunity to work from anywhere, up to 10 days per year * Space to connect: Beyond the desk, we make time for weekly catch-ups, seasonal celebrations, and have a kitchen that’s always stocked! Our Commitment to Diversity, Equity and Inclusion We’re an equal opportunities employer. And proud of it. Every applicant and employee is afforded the same opportunities regardless of race, colour, ancestry, religion, sex, national origin, sexual orientation, age, citizenship, marital status, disability, gender, gender identity or expression, or veteran status. This will never change. Read our Equality, Diversity & Inclusion policy here. Our Commitment to Safeguarding Multiverse is committed to safeguarding and promoting the welfare of our learners. We expect all employees to share this commitment and adhere to our Safeguarding Policy, our Prevent Policy and all other Multiverse company policies. Successful applicants will be required to undertake at least a Basic check via the Disclosure Barring Service (DBS). For roles that will involve a Regulated Activity, successful applicants must also undergo an Enhanced DBS check, including a Children’s Barred List check and a Prohibition Order check. Roles involving Regulated Activity may interact with vulnerable groups, therefore are exempt from the Rehabilitation of Offenders Act 1974 meaning applicants are required to declare any convictions, cautions, reprimands, and final warnings. Providing false information is an offence and could result in the application being rejected or summary dismissal if the applicant has been selected, and possible referral to the police and the DBS.