Job title: Panel Analyst (Data Analyst)

Locations: Manchester (hybrid working)

Role overview

Supporting all product lines, across all brands within Markerstudy Distribution – this role sits in the Analytics and Enrichment department which is focussed primarily on insurer panel analytics  – ensuring the broking arm has access to market leading insurer prices through a combination of robust performance monitoring and advanced data analytics, as well as marketing analytics – including campaign data selections and developing insights to drive improved marketing performance across a range of channels.

Markerstudy is a leading provider of private insurance in the UK, insuring around 5% of the private cars on the UK roads, 20% of commercial vehicles and over 30% of motorcycles in total premium levels of circa £1b. Most of Markerstudy’s business is written as the insurance pricing provider behind household names such as Tesco, Sainsbury’s, O2, Halifax, AA, Saga and Lloyds Bank to list a few.

Key Responsibilities:

• Serve as a point of contact in the area of Analytics
• Support the delivery of analytics provided to the group via analysis, testing, modelling, profiling and reporting.
• Conduct analysis, at appropriate level of sophistication/depth to deliver market-leading broker insight offerings
• Identify and fulfil new data-driven opportunities in the areas of customer acquisition, upsell, cross-sell and retention
• Ensuring appropriate technical documentation is in place for regular processes.
• Comply with all regulatory obligations with regards to customer data, competition law and other relevant guidance/ legislation

Key Skills and Experience:

• Bachelor’s degree or equivalent in relevant numerical discipline (eq Mathematics, Computer Science, Data Science etc).
• Academic or industry knowledge of a range of statistical techniques and concepts e.g., logistic regression, clustering, decision trees, machine learning
• Highly numerate with extensive knowledge of analytical techniques and the application of these in a business context
• Ability to communicate information clearly and concisely to varied and sometimes non-technical audiences

Desirable

• Postgraduate qualification in relevant numerical discipline (eq Mathematics, Computer Science, Data Science etc)
• Experience of interacting with Data Warehouse/Lake/Lakehouse solutions eg via Databricks, Snowflake, MS Fabric
• Experience of developing analytics solutions using a ‘notebook’/ipynb format with clear markdowns
• Experience of deployment and maintenance of statistical/machine learning models (via MLOps/Git or similar)

Behaviours:

• Collaborative and team player
• Logical thinker with a professional and positive attitude
• Passion to innovate and improve processes