Technical Pricing Analyst

Manchester
11 months ago
Applications closed

Related Jobs

View all jobs

Head of Data Science

Technical Data Architect

Technical Data Analyst

Business Intelligence Analyst

Data Governance & Security Consultant

Full Stack Data Engineer (SC cleared)

Job Title: Technical Pricing Analyst 

Locations: Manchester, flexible hybrid position. 

Role Overview

This role is for Atlanta Group, part of the Markerstudy Group.

Markerstudy Group are looking for a Technical Pricing Analyst to help deliver the technical pricing strategy for our delegated authority insurance products. 

Your role is critical in ensuring accurate risk pricing are developed and refreshed on a regular basis to improve the predictiveness of our technical pricing models.

You’ll collaborate with cross-functional teams, develop pricing recommendations, manage product performance and develop your team of analysts alongside continually improving our pricing capability.

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

Lead components of each risk pricing project playing a significant role in risk model builds and refreshes across all product lines.

Work closely with stakeholders across the business to ensure data used for risk modelling is consistent, efficient and suitable for modelling.

Contribute to delivering our commercial objectives by continuing to build and maintain our risk pricing models efficiently to achieve profitable growth for the wider business and our capacity partners.

Manage relationships with our key internal and external stakeholders.

Supporting and contribute to key projects across the business as a pricing SME.

Work with the Portfolio Management and Underwriting teams on risk appetite, product development and innovation to ensure that pricing models have the appropriate level of expert input.

Communicate effectively and build trusting relationships by understanding and meeting expectations of key internal and external stakeholders.

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 Data Science Tools Do You Need to Know to Get a Data Science Job?

If you’re trying to break into data science — or progress your career — it can feel like you are drowning in names: Python, R, TensorFlow, PyTorch, SQL, Spark, AWS, Scikit-learn, Jupyter, Tableau, Power BI…the list just keeps going. With every job advert listing a different combination of tools, many applicants fall into a trap: they try to learn everything. The result? Long tool lists that sound impressive — but little depth to back them up. Here’s the straight-talk version most hiring managers won’t explicitly tell you: 👉 You don’t need to know every data science tool to get hired. 👉 You need to know the right ones — deeply — and know how to use them to solve real problems. Tools matter, but only in service of outcomes. So how many data science tools do you actually need to know to get a job? For most job seekers, the answer is not “27” — it’s more like 8–12, thoughtfully chosen and well understood. This guide explains what employers really value, which tools are core, which are role-specific, and how to focus your toolbox so your CV and interviews shine.

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

If you’re applying for data science roles in the UK, it’s crucial to understand what hiring managers focus on before they dive into your full CV. In competitive markets, recruiters and hiring managers often make their first decisions in the first 10–20 seconds of scanning an application — and in data science, there are specific signals they look for first. Data science isn’t just about coding or statistics — it’s about producing insights, shipping models, collaborating with teams, and solving real business problems. This guide helps you understand exactly what hiring managers look for first in data science applications — and how to structure your CV, portfolio and cover letter so you leap to the top of the shortlist.

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

Data science has become one of the most visible and sought-after careers in the UK technology market. From financial services and retail to healthcare, media, government and sport, organisations increasingly rely on data scientists to extract insight, guide decisions and build predictive models. Universities have responded quickly. Degrees in data science, analytics and artificial intelligence have expanded rapidly, and many computer science courses now include data-focused pathways. And yet, despite the volume of graduates entering the market, employers across the UK consistently report the same problem: Many data science candidates are not job-ready. Vacancies remain open. Hiring processes drag on. Candidates with impressive academic backgrounds fail interviews or struggle once hired. The issue is not intelligence or effort. It is a persistent skills gap between university education and real-world data science roles. This article explores that gap in depth: what universities teach well, what they often miss, why the gap exists, what employers actually want, and how jobseekers can bridge the divide to build successful careers in data science.