Be at the heart of actionFly remote-controlled drones into enemy territory to gather vital information.

Apply Now

Data Scientist (Retail Pricing)

Policy Expert
City of London
1 day ago
Create job alert

🚀 Are you ready to transform the insurance industry?


Policy Expert is a forward-thinking business that loves to get things done. Leveraging proprietary technology and smart data, we offer reliable products and a wow customer experience.


Having achieved rapid growth since being founded in 2011, we’ve won over 1.5 million customers in Home, Motor and Pet insurance and have been ranked the UK’s No.1-rated home insurer by Review Centre since 2013. 🏆


This is an exciting time for us as we expand our Data Science capabilities to support our ambitious growth plans in insurance pricing/counter fraud/underwriting. Retail pricing is at the heart of our business – finding the right premium to offer each customer, balancing competitiveness, fairness, and profitability. We’re investing heavily in data science and ML engineering to take our pricing models to the next level, with the customer at the centre of everything we do. With a wealth of internal and external datasets and cutting-edge technologies, we have the opportunity to deliver innovative, market-leading pricing solutions that customers can trust.


What you’ll be doing


As a Data Scientist, you will be developing, testing, and deploying advanced statistical and machine learning models to optimise customer premiums and support our pricing/UW/counter fraud strategy. You’ll collaborate closely with pricing, underwriting, and commercial teams, as well as ML Engineers, to ensure models are implemented effectively and deliver measurable value. Activities will include:



  • Building predictive and optimisation models to determine the best premium to offer across home, motor, and pet insurance products.
  • Designing and running pricing experiments (including A/B tests and market tests) to understand customer behaviour, retention, and conversion.
  • Developing real-time pricing and decisioning solutions that balance volume, profitability, and risk appetite.
  • Working within a cross-functional squad (pricing managers, underwriters, ML engineers, analysts) to deliver pricing improvements and business outcomes.
  • Influencing priorities using data-driven insights and quantifying the commercial impact of initiatives.
  • Monitoring model performance and ensuring regulatory and fairness considerations are built into our pricing solutions.
  • Being a champion of retail pricing science across the business, explaining the value of data-driven pricing strategies to non-technical stakeholders.

Who are you:



  • Experience developing and deploying statistical and machine learning models in the insurance retail pricing/counter fraud/UW context.
  • Strong programming skills in Python, with a working knowledge of SQL, and experience analysing large, complex datasets.
  • Strong knowledge of statistical modelling and optimisation techniques relevant to pricing and customer behaviour.
  • A desire to interact with stakeholders (pricing, underwriting, product) and translate business needs into practical modelling solutions.
  • Strong teamwork and communication skills, with the ability to explain complex pricing or modelling concepts to a non-technical audience.
  • Ability to design, monitor, and interpret pricing tests and experiments.
  • Understanding of the need for clean, reliable data and experience working with Data Engineers to define data requirements.
  • Familiarity with software engineering best practices and writing efficient, production-ready code.
  • Must have worked with Pricing within a UK general insurance market and regulatory environment (e.g., FCA pricing regulations, fair value) would be an advantage.
  • Experience with cloud-based ML platforms such as GCP Vertex AI (or similar) is desirable.

📍 This role will be based in our London office in a 50/50 Hybrid mode.


💸 We match your pension contributions up to 7%


📚 Learning budget of £1,000 a year + Study leave (with encouragement to use it)


😁 Enhanced maternity & paternity


🚉 Travel season ticket loan


🎟️ Access to a wide selection of London O2 events and use of a Private Lounge


What We Stand for and Next Steps


“We pride ourselves on being an equal opportunity employer. We treat all applications equally and recruit based solely on an individual’s skills, knowledge, and experience. The quality and growing diversity of our team is a testament to this commitment”


At Policy Expert, we are committed to fostering an inclusive and supportive environment for all candidates. If you require any reasonable adjustments during the interview process to accommodate your needs, please do not hesitate to let us know. We are dedicated to ensuring every candidate has an equal opportunity to succeed and will work with you to provide the necessary support.


We aim to be in touch within 14 working days of receiving your application – you will be notified if your application is successful or unsuccessful. Please be encouraged to apply even if you do not meet all the requirements.


Interested in building your career at Policy Expert? Get future opportunities sent straight to your email.


#J-18808-Ljbffr

Related Jobs

View all jobs

Data Scientist

Data Scientist

Data Scientist

Data Scientist

Data Scientist

Data Scientist Python Software - London (IT) / Freelance

Subscribe to Future Tech Insights for the latest jobs & insights, direct to your inbox.

By subscribing, you agree to our privacy policy and terms of service.

Industry Insights

Discover insightful articles, industry insights, expert tips, and curated resources.

Machine Learning Recruitment Trends 2025 (UK): What Job Seekers Need To Know About Today’s Hiring Process

Summary: UK machine learning hiring has shifted from title‑led CV screens to capability‑driven assessments that emphasise shipped ML/LLM features, robust evaluation, observability, safety/governance, cost control and measurable business impact. This guide explains what’s changed, what to expect in interviews & how to prepare—especially for ML engineers, applied scientists, LLM application engineers, ML platform/MLOps engineers and AI product managers. Who this is for: ML engineers, applied ML/LLM engineers, LLM/retrieval engineers, ML platform/MLOps/SRE, data scientists transitioning to production ML, AI product managers & tech‑lead candidates targeting roles in the UK.

Why Machine Learning Careers in the UK Are Becoming More Multidisciplinary

Machine learning (ML) has moved from research labs into mainstream UK businesses. From healthcare diagnostics to fraud detection, autonomous vehicles to recommendation engines, ML underpins critical services and consumer experiences. But the skillset required of today’s machine learning professionals is no longer purely technical. Employers increasingly seek multidisciplinary expertise: not only coding, algorithms & statistics, but also knowledge of law, ethics, psychology, linguistics & design. This article explores why UK machine learning careers are becoming more multidisciplinary, how these fields intersect with ML roles, and what both job-seekers & employers need to understand to succeed in a rapidly changing landscape.

Machine Learning Team Structures Explained: Who Does What in a Modern Machine Learning Department

Machine learning is now central to many advanced data-driven products and services across the UK. Whether you work in finance, healthcare, retail, autonomous vehicles, recommendation systems, robotics, or consumer applications, there’s a need for dedicated machine learning teams that can deliver models into production, maintain them, keep them secure, efficient, fair, and aligned with business objectives. If you’re hiring for or applying to ML roles via MachineLearningJobs.co.uk, this article will help you understand what roles are typically present in a mature machine learning department, how they collaborate through project lifecycles, what skills and qualifications UK employers look for, what the career paths and salaries are, current trends and challenges, and how to build an effective ML team.