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Decision Science Modeller

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Job Description - Decision Science Modeller

Decision Science Modeller
£40,000
Leeds (Hybrid Working Available)

An excellent opportunity for a high-performing Master's or PhD graduate-or a modeller with 1-2 years' experience in financial services-to step into a data-driven analytical role. You'll support the development of scorecards, pricing strategies, and segmentation models within a leading player in the consumer finance space.

About the Organisation
This is a well-established and growing financial services firm operating across the UK and Europe. The business focuses on acquiring and managing diverse consumer finance portfolios and is known for its customer-first approach. They place a strong emphasis on ethical treatment, compliance, and data-led decision-making, working with a wide range of clients across financial services and utilities.

The Role
Joining a high-performing Decision Science team, your remit will involve end-to-end predictive modelling and analytics. You'll play a vital role in shaping strategic decisions across the business.

Key Responsibilities:

  • Develop and optimise scorecards and pricing models using statistical and machine learning methods

  • Translate complex data into actionable insights for commercial stakeholders

  • Work closely with operational teams to embed models and monitor performance

  • Drive innovation by supporting the team's growth in advanced analytics and machine learning techniques

Candidate Profile

You'll need either:

  • A Master's or PhD in a STEM subject
    or

  • 1-2 years' relevant experience in a modelling role within financial services

Essential:

  • Strong coding ability in Python

Desirable:

  • SQL proficiency

  • Exposure to credit risk, collections, or pricing

  • Knowledge of machine learning methods and practical applications

Package Overview

  • £40,000 base salary

  • Annual performance bonus

  • Pension scheme

  • Private healthcare

Register your interest by sending your CV to Daniel Abbasi via the apply link on this page.

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