Lead Portfolio Pricing Analyst (Motor)

Manchester
9 months ago
Applications closed

Related Jobs

View all jobs

Portfolio Revenue & Debt Data Analyst

Portfolio Revenue & Debt Data Scientist

Portfolio Revenue & Debt Data Analyst - Swindon, Wiltshire

Portfolio Revenue & Debt Data Scientist - Swindon, Swindon

Data Governance Lead

Data Scientist / AI Engineer

Lead Portfolio Pricing Analyst (Portfolio Management)

Location: This position is largely remote, with the occasional travel. We have offices in Manchester and London.

Role Overview

We’re looking for a Lead Portfolio Pricing Analyst to join our expanding Portfolio Management team within the fast-paced and ambitious world of personal lines underwriting. This is an exciting opportunity to take a lead role in shaping our pricing strategies and performance monitoring frameworks while contributing to the profitability and growth of our product portfolio.

In this senior role, you will lead key aspects of portfolio performance analysis and pricing interventions, using a blend of analytical expertise, commercial acumen, and cross-functional collaboration to influence key business decisions. You’ll support and mentor a small team of analysts, play a key role in driving innovation and pricing best practice, and act as a trusted expert across the business.

The Pricing portfolio management team is responsible for developing new modelling techniques and processes and building and refreshing the risk models that underpin our rates that need to operate effectively in the aggregator channels.

Key Responsibilities

Lead the design and evolution of our performance monitoring frameworks across product lines

Drive tactical pricing initiatives and optimise pricing opportunities through robust analytical insights

Provide strategic oversight of pricing recommendations that improve portfolio performance and meet profitability targets

Collaborate with Underwriting, Technical Modelling, and Data teams to inform product development, technical model calibration, and risk cost feedback loops

Manage stakeholder relationships across the business, ensuring clear communication of analytical insight and pricing impacts

Mentor and develop junior analysts, fostering a culture of learning, innovation, and continuous improvement

Contribute to and help shape the delivery of the Pricing roadmap in line with our long-term strategy and growth objectives

Key Skills and Experience

Substantial experience within Personal Lines Pricing, ideally including team or project leadership

Proficiency in predictive modelling techniques such as Logistic Regression, GBMs, Elastic Net GLMs, GAMs, Decision Trees, Random Forests, Neural Nets, and Clustering

Strong skills in R, Python, PySpark, SAS, or SQL

Proven ability to interpret performance data and make commercial recommendations

Experience with WTW’s Radar and Emblem software is preferred

Excellent communication skills, with the ability to translate complex analysis into clear, actionable insight

A good quantitative degree in Mathematics, Statistics, Engineering, Physics, Computer Science or Actuarial Science

Behaviours

Self-motivated, with a passion for coaching and developing others

A logical thinker with a proactive, positive mindset

Enthusiastic about innovation, with a keen eye for improving processes and challenging the status quo

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.