Senior Data Science Manager, Pricing

In this role, you will lead pricing analytics to understand loss ratio drivers and steer commercial actions. You will bridge analytics with business strategy across claims, finance and product, shaping how pricing informs growth. You’ll manage a small team of pricing data scientists and drive cross-functional investigations into risk signals. This is a high-impact, visible role at Marshmallow as it scales, offering autonomy to influence pricing decisions and stakeholder communication. Join a fast-growing team that values curiosity, collaboration and clear storytelling.

• 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
• 25 days holiday plus bank holidays

• Own loss ratio view for the portfolio and translate into commercial action considering internal and market conditions.
• Lead pricing performance discussions with internal and external stakeholders, delivering clear insights to non-technical audiences.
• Manage and develop a small group of pricing data scientists, raising quality of work.
• Drive cross-functional investigations into claims and risk signals with business partners.
• Shape deployment of pricing data scientists across workstreams as the team’s operating model evolves.

• Experience as a data scientist or data science manager in risk-focused domains such as pricing, credit decisioning or fraud.
• Experience in commercially driven environments where performance metrics guide decisions.
• Ability to form clear, commercially grounded views from technical analysis and communicate to senior stakeholders.
• Experience making sound decisions under data uncertainty and incomplete information.
• Collaborative approach with adjacent functions to link pricing insights to business performance.

• commercially minded, business storytelling
• clear communication with non-technical audiences
• curiosity and challenging assumptions
• data science in risk/pricing domains
• analysis of loss ratios and pricing performance
• stakeholder communication of analytical outputs