Principal Data Scientist at marshmallow

✨ The Stack take on this marshmallow job

• 🏢 marshmallow has 6 jobs here, 6 with visa sponsorship see all
• 📊 28 Data Scientist jobs in the United Kingdom in the last 60 days see them
• 🛂 Skilled Worker visa. Standard minimum salary: £41,700 a year guide
• 💡 No salary shown. Ask marshmallow if it is above the minimum.

Updated 2026-11-05, from our data and official sources.

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About the role

Responsibilities:
- Provide strategic technical leadership across Claims to define and deliver multi-year ML and Generative AI roadmaps (fraud, validation, automation).
- Architect robust, scalable decisioning systems that integrate models, data pipelines and operational workflows across teams.
- Drive platform investments (monitoring, QA, feedback loops) and governance to ensure safe, measurable model behaviour in production.
- Challenge and influence product and engineering proposals to keep solutions maintainable, secure and aligned to business objectives.
- Lead experimentation strategy, set statistical standards and ensure model uncertainty and performance are communicated to senior stakeholders.
- Coach senior data scientists and shape cross-functional practices for deployment, validation and ongoing model operations.

Requirements:
- Extensive experience delivering production ML and GenAI/LLM systems at scale and shaping technical strategy across teams.
- Strong background in system architecture, monitoring, governance and model lifecycle management.
- Demonstrated ability to influence senior stakeholders and translate business goals into technical programmes.
- Deep statistical rigour, experimentation experience and familiarity with operational constraints in insurance/financial products.
- Excellent collaboration and leadership skills; ability to mentor technical staff and work closely with engineering and product.

Responsibilities

Provide technical leadership for data science within the Claims department, focusing on fraud detection and risk decisioning. Design and deploy production-grade machine learning and Generative AI systems to automate and improve the claims journey.

Requirements

Requires significant commercial experience in end-to-end machine learning and hands-on experience shipping Generative AI systems in production. Candidates must possess a strong statistical foundation and the ability to influence technical direction across cross-functional teams.

Key skills

• Machine learning — in 291 other listings on The Stack
• Generative AI — in 58 other listings on The Stack
• LLM — in 51 other listings on The Stack
• Risk-based decisioning — in 1 other listing on The Stack
• Fraud detection — in 10 other listings on The Stack
• Statistical rigour — in 1 other listing on The Stack
• System-level thinking — in 3 other listings on The Stack
• Technical leadership — in 133 other listings on The Stack
• Stakeholder management
• Production deployment
• Data science
• Product strategy
• Engineering collaboration
• Automation
• Experimentation

On The Stack (as of 2026-11-05)

• marshmallow has 5 other listings on The Stack, 5 of them marked as offering visa sponsorship.
• 214 visa-sponsored jobs in the United Kingdom on The Stack, posted in the last 60 days.
• 68 visa-sponsored data scientist jobs in Europe on The Stack, posted in the last 60 days.

Frequently asked questions about marshmallow

Is this Principal Data Scientist role still open?

It was posted on 2026-10-06 and is listed as active on The Stack. Confirm on marshmallow's application page before applying.

Does marshmallow offer visa sponsorship?

This listing is marked as offering visa sponsorship. Eligibility rules are set by the employer, so check the details on marshmallow's site.

Where is this role located?

The listing gives the location as London, United Kingdom. It is marked hybrid.

What skills does this role ask for?

The listing names: Machine learning, Generative AI, LLM, Risk-based decisioning, Fraud detection, Statistical rigour, System-level thinking, Technical leadership, Stakeholder management, Production deployment, Data science, Product strategy, Engineering collaboration, Automation, Experimentation.