Decision Science Modeller - #1686513

Harnham


Date: 14 hours ago
City: Leeds
Contract type: Full time
Work schedule: Full day
Harnham

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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