Senior Pricing Analyst

Manchester
8 months ago
Applications closed

Related Jobs

View all jobs

Senior Data Scientist

Senior Pricing Data Scientist

Senior Geospatial Data Scientist

Data Science Manager

Machine Learning Engineer

Data Science Manager

Job Title: Senior Pricing Analyst

Locations: Manchester (flexible)

Role Overview

Markerstudy Group are looking for a Senior Pricing Analyst to help build and shape our pricing models. You will help monitor our portfolio and deliver innovative pricing solutions within the Retail Pricing team.

Joining our retail pricing team, you will be keeping a close eye on trading across different channels and insurance products. You will have previous experience in general insurance pricing and be familiar with the tools of the trade, such as SAS, Python, RStudio, SQL, Emblem and Radar. With your naturally inquisitive mindset, you will be well versed in the UK personal lines insurance space, and understand how the personal lines insurance market works. Always open to change, you have a keen eye for the continuous improvement of process.

As a Senior Pricing Analyst, you will use your advanced analytical skills to:

Conducting retail price optimisation analysis/modelling

developing customer propensity and Life Time Value (LTV) models to produce the different models and SAS or SQL for data analysis

Create innovative data solutions finding new ways to mine insight & present data

Build and maintain sophisticated models, prioritising a range of data science techniques

Advance the adoption of data science & statistical techniques

Communicate results to key decision makers across the business for action based on the results of pricing analysis

Collaborate with peers in pricing, underwriting and data science

will generate insight to help make commercial decisions and strategic changes to prices to meet budget requirements.

Key Skills and Experience:

Previous experience within general insurance pricing

Experience with some of the following predictive modelling techniques; Logistic Regression, GBMs, Elastic Net GLMs, GAMs, Decision Trees, Random Forests, Neural Nets and Clustering

Experience in statistical and data science programming languages (e.g. R, Python, PySpark, SAS, SQL)

A good quantitative degree (Mathematics, Statistics, Engineering, Physics, Computer Science, Actuarial Science)

Experience of WTW’s Radar software is preferred

Proficient at communicating results in a concise manner both verbally and written

Behaviours:

Self-motivated with a drive to learn and develop

Logical thinker with a professional and positive attitude

Passion to innovate, improve processes and challenge the norm

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.

How Many AI Tools Do You Need to Know to Get an AI Job?

If you are job hunting in AI right now it can feel like you are drowning in tools. Every week there is a new framework, a new “must-learn” platform or a new productivity app that everyone on LinkedIn seems to be using. The result is predictable: job seekers panic-learn a long list of tools without actually getting better at delivering outcomes. Here is the truth most hiring managers will quietly agree with. They do not hire you because you know 27 tools. They hire you because you can solve a problem, communicate trade-offs, ship something reliable and improve it with feedback. Tools matter, but only in service of outcomes. So how many AI tools do you actually need to know? For most AI job seekers: fewer than you think. You need a tight core toolkit plus a role-specific layer. Everything else is optional. This guide breaks it down clearly, gives you a simple framework to choose what to learn and shows you how to present your toolset on your CV, portfolio and interviews.

What Hiring Managers Look for First in AI Job Applications (UK Guide)

Hiring managers do not start by reading your CV line-by-line. They scan for signals. In AI roles especially, they are looking for proof that you can ship, learn fast, communicate clearly & work safely with data and systems. The best applications make those signals obvious in the first 10–20 seconds. This guide breaks down what hiring managers typically look for first in AI applications in the UK market, how to present it on your CV, LinkedIn & portfolio, and the most common reasons strong candidates get overlooked. Use it as a checklist to tighten your application before you click apply.

The Skills Gap in AI Jobs: What Universities Aren’t Teaching

Artificial intelligence is no longer a future concept. It is already reshaping how businesses operate, how decisions are made, and how entire industries compete. From finance and healthcare to retail, manufacturing, defence, and climate science, AI is embedded in critical systems across the UK economy. Yet despite unprecedented demand for AI talent, employers continue to report severe recruitment challenges. Vacancies remain open for months. Salaries rise year on year. Candidates with impressive academic credentials often fail technical interviews. At the heart of this disconnect lies a growing and uncomfortable truth: Universities are not fully preparing graduates for real-world AI jobs. This article explores the AI skills gap in depth—what is missing from many university programmes, why the gap persists, what employers actually want, and how jobseekers can bridge the divide to build a successful career in artificial intelligence.