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Data Scientist (Claims) - Job Opportunity at Marshmallow

London, GB

Full-time

Mid-level

Posted: June 19, 2025

Hybrid

£65,000 - £85,000 per year. This estimate reflects the mid-level experience requirement combined with London's competitive tech market and the specialized nature of insurance domain expertise. The role's focus on cutting-edge AI applications and direct business impact justifies positioning in the upper portion of the mid-level range, with potential for performance bonuses adding 10-20% additional compensation.

Benefits

Flexible hybrid work arrangement allowing 2-3 days in-office with remainder remote, providing optimal work-life balance

Performance-based competitive bonus scheme designed to reward high achievers and align compensation with results

Monthly flexible benefits budget of £50 through Ben Mastercard for personalized spending on subscriptions, courses, or lifestyle choices

Four-week fully paid sabbatical leave after four years of service, supporting long-term employee retention and personal development

Four weeks annual work-from-anywhere policy with no office attendance requirements, enabling global remote work flexibility

Comprehensive mental health support through Oliva platform providing professional therapy and counseling access

Personal learning and development budget plus two dedicated annual training days for continuous skill advancement

Premium private healthcare through Vitality including gym membership discounts and wellness device subsidies

Medical cash plan covering dental, optical, and physiotherapy expenses to reduce out-of-pocket healthcare costs

Technology purchase scheme providing access to latest equipment at reduced rates

Generous 33-day annual holiday allowance exceeding UK statutory requirements

Employer pension contributions supporting long-term financial security

Cycle-to-work scheme promoting sustainable commuting with tax benefits

Regular team and company-wide social events fostering community and culture engagement

Key Responsibilities

Lead end-to-end development and deployment of data science solutions that revolutionize claims triage, prioritization, and resolution processes with quantifiable impact on operational costs, processing speed, and customer satisfaction metrics

Design and implement sophisticated predictive modeling systems that enable automated decision-making and enhance human judgment capabilities across risk assessment, fraud detection, case prioritization, and exception handling workflows

Drive cross-functional collaboration with engineering teams, product managers, and claims domain experts to seamlessly integrate machine learning models into production systems and operational tools

Champion experimentation-driven culture by designing and executing A/B tests and comprehensive analytics frameworks to validate business value and model performance

Define and establish key performance indicators and analytical processes that enable continuous monitoring, evaluation, and optimization of claims handling performance

Pioneer the application of cutting-edge AI technologies including agentic systems and large language models to streamline operational workflows and provide real-time decision support to claims professionals

Requirements

Education

PhD or masters degree from a top institution

Experience

At least 2+ years of professional experience in data science or similar roles

Required Skills

Experience building and deploying ML models that deliver measurable business results Understanding of how to evaluate performance within risk decisioning, including performance trade-offs and operational considerations Familiarity with A/B testing and measuring model impact Proficiency in Python and SQL Experience in insurance, operations, or other complex service environments Experience working with unstructured data (e.g. documents, free text, images) Knowledge of MLOps best practices and model monitoring Experience with experimentation frameworks and KPI tracking Strong communication skills Experience designing, building and deploying Generative AI applications or Retrieval-Augmented Generation (RAG) systems in operational or customer facing environments

Sauge AI Market Intelligence

Industry Trends

The insurance technology (InsurTech) sector is experiencing unprecedented growth with artificial intelligence and machine learning becoming fundamental differentiators rather than optional enhancements. Companies are investing heavily in AI-driven claims processing to reduce operational costs and improve customer experience, with the global InsurTech market expected to reach $152 billion by 2030. This trend is particularly pronounced in auto insurance where telematics data and predictive analytics are revolutionizing risk assessment and claims handling. Regulatory compliance in financial services is driving increased demand for explainable AI and robust model governance frameworks. Data scientists in insurance must now balance model performance with interpretability requirements, creating new challenges and opportunities for professionals who can navigate both technical excellence and regulatory constraints. The migration toward real-time decision making in insurance claims is accelerating, with companies seeking to process and resolve claims within hours rather than days. This shift requires sophisticated automation frameworks and human-in-the-loop systems that can handle edge cases while maintaining high throughput and accuracy. Generative AI and large language models are emerging as game-changers in insurance operations, particularly for document processing, customer communication, and fraud detection. Organizations are actively seeking data scientists who can implement RAG systems and agentic workflows to transform traditional insurance processes.

Role Significance

Likely part of a 4-6 person data science team within the Claims Tribe, collaborating closely with 2-3 engineers, 1-2 product managers, and multiple claims domain experts. The tribal structure suggests a matrix organization where the role will interface with 15-25 stakeholders across acquisition and retention functions.

This is a mid-to-senior individual contributor role with significant autonomy and business impact. The position requires independent project ownership and cross-functional leadership without direct management responsibilities. The role sits at the intersection of technical execution and strategic business influence, making it ideal for experienced practitioners ready to drive organizational transformation through data science.

Key Projects

Development of real-time fraud detection systems using ensemble methods and anomaly detection algorithms to identify suspicious claims patterns and reduce financial losses Implementation of automated claims triage systems that can classify and route claims based on complexity, urgency, and required expertise, reducing processing time by 40-60% Creation of predictive models for claims cost estimation that help reserves management and enable proactive case management strategies Design and deployment of customer communication automation using NLP and generative AI to provide personalized updates and reduce manual intervention requirements Building comprehensive experimentation frameworks for testing new claims handling processes and measuring their impact on customer satisfaction and operational efficiency

Success Factors

Technical versatility in both traditional machine learning and cutting-edge AI technologies, with the ability to select appropriate tools based on business context rather than following trends. Success requires balancing model sophistication with operational simplicity, ensuring solutions can be maintained and understood by non-technical stakeholders. Strong business acumen combined with technical depth, enabling translation of complex insurance domain challenges into data science solutions. The most successful candidates will demonstrate curiosity about insurance operations and ability to identify high-impact opportunities that others might overlook. Collaborative mindset with excellent communication skills across technical and business stakeholders. The role requires building trust with claims professionals who may be skeptical of automation, while also working effectively with engineers to ensure robust production deployments. Pragmatic approach to model development with focus on measurable business outcomes rather than academic perfection. Success depends on delivering working solutions quickly and iterating based on feedback, rather than pursuing theoretical optimization. Adaptability and continuous learning orientation, particularly around emerging AI technologies. The insurance industry is rapidly evolving, and success requires staying current with both technological advances and regulatory changes that impact model development and deployment.

Market Demand

High demand with limited supply of qualified candidates. The intersection of insurance domain knowledge, advanced AI/ML skills, and practical deployment experience creates a talent shortage. Companies are actively competing for professionals who can bridge the gap between theoretical data science and operational insurance applications.

Important Skills

Critical Skills

Python and SQL proficiency represents the foundation for all data manipulation, model development, and production deployment activities. These skills must be advanced enough to handle complex data pipelines, model training workflows, and integration with existing systems. The insurance domain requires working with large, complex datasets where efficient data processing is essential for meeting business timelines. Machine learning model development and deployment experience is absolutely essential as the role centers on building production systems that directly impact business operations. This includes understanding of model selection, hyperparameter tuning, validation strategies, and deployment architectures. Insurance applications require models that are both accurate and interpretable, adding complexity to the development process. A/B testing and experimentation framework knowledge is critical for validating model performance and business impact in a regulated environment. Insurance companies must demonstrate that new approaches actually improve outcomes, making rigorous experimental design and statistical analysis fundamental to success. Risk decisioning and performance evaluation understanding is essential for navigating the insurance domain where model errors have direct financial consequences. This includes knowledge of precision-recall tradeoffs, cost-sensitive learning, and understanding how model performance translates to business metrics like loss ratios and customer satisfaction.

Beneficial Skills

Insurance domain experience provides significant competitive advantage by enabling faster context acquisition and better problem identification. Professionals with insurance background can more quickly identify high-impact opportunities and avoid common pitfalls that affect model performance in regulated environments. Generative AI and RAG system experience is increasingly valuable as these technologies transform insurance operations. Professionals with hands-on experience implementing these systems in production environments will have significant advantages in driving innovation and efficiency improvements. MLOps and model monitoring expertise becomes crucial as organizations scale their AI capabilities and face increasing regulatory scrutiny. Understanding of model governance, drift detection, and automated retraining systems will be essential for senior-level progression. Communication and stakeholder management skills are differentiating factors that enable data scientists to drive adoption of their solutions and build trust with business stakeholders who may be skeptical of AI automation in critical business processes.

Unique Aspects

Direct impact on critical business metrics including loss ratios and operational efficiency, providing immediate visibility into the business value of data science work and creating strong performance feedback loops

Opportunity to work with cutting-edge AI technologies including agentic systems and large language models in a production environment with real customer impact, rather than experimental or research contexts

Exposure to the complete insurance value chain from customer acquisition through claims resolution, providing comprehensive understanding of how data science drives business outcomes across multiple functions

Integration with human-centered design principles where AI augments rather than replaces human expertise, creating opportunities to develop sophisticated human-in-the-loop systems

Access to rich, diverse datasets including telematics, demographic, behavioral, and unstructured data sources that enable sophisticated modeling approaches and innovative feature engineering

Career Growth

Career progression typically occurs within 18-36 months for high performers in fast-growing InsurTech companies. The rapid pace of technological change and business growth creates accelerated advancement opportunities compared to traditional insurance companies.

Potential Next Roles

Senior Data Scientist or Principal Data Scientist roles with expanded scope across multiple business areas and leadership of complex AI initiatives Data Science Manager positions leading teams of 3-8 data scientists and driving organizational AI strategy Product Manager roles focusing on AI/ML products, leveraging deep technical understanding to guide product development AI/ML Engineering roles with focus on scalable model deployment and MLOps infrastructure Consulting roles with major firms specializing in insurance technology transformation and AI implementation

Company Overview

Marshmallow

Marshmallow operates as a technology-driven insurance disruptor targeting underserved markets, particularly immigrants and international drivers who face challenges with traditional insurance providers. The company has achieved significant scale with over one million insured drivers while maintaining a startup culture focused on rapid innovation and customer-centric product development. Their expansion beyond auto insurance signals ambitious growth plans and diversification strategy.

Positioned as a leading UK InsurTech company with strong market presence in the immigrant and expatriate insurance segment. The company has demonstrated product-market fit and operational scalability, making it an attractive option for professionals seeking exposure to both startup dynamics and established business operations.

Strong UK market presence with London headquarters providing access to top-tier talent and investment community. The location offers excellent networking opportunities within the European fintech ecosystem and regulatory environment that supports innovation while maintaining consumer protection standards.

Culture emphasizes rapid experimentation, cross-functional collaboration, and individual ownership with clear alignment toward business objectives. The tribal organizational structure and biannual planning cycles suggest sophisticated operational maturity while preserving startup agility and innovation focus.

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Data Scientist (Claims) - Job Opportunity at Marshmallow

Benefits

Key Responsibilities

Requirements

Education

Experience

Required Skills

Sauge AI Market Intelligence

Industry Trends

Role Significance

Key Projects

Success Factors

Market Demand

Important Skills

Critical Skills

Beneficial Skills

Unique Aspects

Career Growth

Potential Next Roles

Company Overview

Marshmallow

Data Sources & Analysis Information

Job Listings Data

Sauge AI Market Intelligence