Job title: Principal Pricing Analyst (Commercial Insurance)
Location: Flexible hybrid working
About the role
Are you ready to use your technical expertise to shape pricing strategies? At Markerstudy, we’re on a mission to redefine how we deliver value to our customers and the business. This is your opportunity to join our Pricing team and play a key role in managing and optimising data solutions that underpin our pricing capability.
Location: Peterborough, Manchester, London, Stoke-on-Trent, Haywards Heath (flexible hybrid working with 2 days per week with the team in office)
In exceptional circumstances we support remote working with occasional travel to our offices.
Why this role matters
As Principal Pricing Analyst (Commercial Insurance) you will be responsible for the retail pricing of our small commercial insurance products offered by Markerstudy Business. To support our small business customers we provide Taxi, Commercial Van, Commercial Liability, and Property covers. These are sold via our call centre, web site and aggregator sites. We have big growth ambitions and have plans to launch new products and expand our offerings via the aggregator sites.
This role sits within Markerstudy Retail, so will benefit from the support and expertise of the Personal Lines retail pricing team. You’ll provide technical leadership on modelling and optimisation, champion best practices, and coach team members to introduce and embed sophisticated personal lines methodologies across our commercial products. Ultimately, you’ll play a pivotal role in driving the evolution of our pricing capability and ensuring we stay ahead in a fast moving market.
What you’ll do
Lead the development and maintenance of predictive models (GLMs) and price optimisation techniques, including machine learning algorithms (GBM), Lifetime Value models (LTV), and fair pricing principles.
Provide technical leadership on WTW Radar and Emblem, driving effective and efficient solutions.
Research, develop, and champion best-practice methods; upskill the team through coaching and code reviews.
Query and engineer large datasets (e.g., Python/R/SQL/PySpark) on modern platforms (e.g., Azure Databricks).
Communicate prici...
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