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Consumer Lending Data Scientist
Datatech
Manchester
Hybride
GBP 80 000 - 100 000
Plein temps
Il y a 15 jours

Résumé du poste

A major financial services group in the UK is seeking a Data Scientist for its consumer lending division. You will work hybrid, enhancing Python-based ML models and utilizing SQL on complex datasets. Your role includes collaborating with stakeholders to shape high-value ML use cases and ensuring effective production workflows. This position offers potential growth in technical skills or leadership within a supportive team focused on knowledge sharing and curiosity.

Qualifications

  • Strong grounding in statistics and or Machine Learning, plus solid Python skills.
  • Confident SQL skills, experience with real world, messy datasets.
  • Experience of the ML lifecycle, especially in production environments.

Responsabilités

  • Building and enhancing Python based ML models across the portfolio.
  • Using SQL on complex datasets to engineer robust features.
  • Partnering with ML Engineers to deploy containerised models.

Connaissances

Statistics
Machine Learning
Python
SQL

Outils

BigQuery
Vertex
Description du poste
Data Scientist - ML & Consumer Lending

South West, UK

Hybrid working, strong salary dependent on experience

The client

South West based, modern office hub, and a major consumer lending portfolio. This is a chance to join a well known financial services group that is investing heavily in data, Applied Data Science, and Machine Learning to stay ahead of the market and improve how it serves its customers.

The business is moving towards cloud native, production grade ML, backed by senior leaders who see Data Science as central to the next chapter of growth. You will sit close to real decision making, working with product, risk, and engineering teams to turn data and ML into tangible customer outcomes.

The role

You will join a growing data science team focused on credit cards and wider consumer lending. Your work will span the full ML lifecycle, from exploratory analysis and model build, through to working with ML Engineers to get models into production and keep them performing as the data strategy matures.

The team is open to Data Scientists at different stages, from those looking to build on a first industry role, through to more experienced practitioners who want broader ownership and influence.

What you will be doing
  • Building and enhancing Python based ML models across the credit card portfolio
  • Using SQL on large, complex datasets to engineer robust, reusable features
  • Partnering with ML Engineers to deploy containerised models and support production ML workflows
  • Working with product and risk stakeholders to identify and shape high value ML and AI use cases
  • Operating within a clear model risk and governance framework, with room to experiment and learn
  • Presenting findings in plain language and helping non specialists act on the insights
What we are looking for
  • Strong grounding in statistics and or Machine Learning, plus solid Python
  • Confident SQL skills and experience with real world, messy datasets
  • Experience of the ML lifecycle, ideally including production or near production environments
  • Exposure to modern cloud and analytics tooling, for example BigQuery or Vertex, and BI tools
  • Interest in credit cards or consumer lending, and how ML and AI can improve fairness and outcomes

You will join a friendly team that values curiosity, knowledge sharing, and personal growth. There is genuine scope to shape how ML and AI are used in the business, whether you see your future in deeper technical expertise or in a more leadership focused path.

If this sounds like the kind of environment you want to be part of, we would love to hear from you.

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* Le salaire de référence se base sur les salaires cibles des leaders du marché dans leurs secteurs correspondants. Il vise à servir de guide pour aider les membres Premium à évaluer les postes vacants et contribuer aux négociations salariales. Le salaire de référence n’est pas fourni directement par l’entreprise et peut pourrait être beaucoup plus élevé ou plus bas.

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