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Principal Data Scientist – Digital Banking & Risk Analytics

Opus Recruitment Solutions
City of London
1 week ago
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Location: London (Hybrid)

Contract Type: Permanent


About the Role

We are working with a leading financial technology innovator to recruit a Principal Data Scientist for a flagship digital banking transformation programme. This role is pivotal in developing advanced machine learning solutions that enhance credit risk modelling, fraud detection, and personalised financial services for millions of customers.


The Opportunity

You will lead the design and deployment of AI-driven systems that power real-time decision-making in areas such as credit scoring, transaction monitoring, and customer segmentation. This is a chance to influence the architecture of next-generation banking platforms while mentoring a team of data scientists and collaborating with engineering and product leaders.


Key Responsibilities

  • Drive end-to-end delivery of data science solutions for digital banking and risk analytics use cases.
  • Build predictive models for credit risk, fraud prevention, and customer behaviour using cutting-edge ML techniques.
  • Partner with product teams to embed intelligent decision engines into core banking workflows.
  • Define best practices for model governance, explainability, and regulatory compliance in financial services.
  • Lead and coach a team of data scientists, ensuring technical excellence and innovation.


What We’re Looking For

  • 10+ years of experience in data science or applied machine learning, ideally within financial services or fintech.
  • Strong expertise in Python, ML frameworks, and cloud-based data platforms (AWS, Azure, or GCP).
  • Proven track record in credit risk modelling, fraud analytics, or similar financial domains.
  • Familiarity with big data technologies (Spark, Hive) and MLOps practices for production-scale deployments.
  • Excellent communication skills to engage stakeholders and simplify complex concepts.


Desirable Extras

  • Experience with regulatory frameworks (e.g., Basel, GDPR) and model explainability tools.
  • Knowledge of NLP for document processing or conversational banking applications.
  • Advanced academic background (MSc or PhD in a quantitative discipline).


Why This Role?

  • Shape the future of digital banking through AI-driven innovation.
  • Work on high-impact projects that combine technical depth with real-world financial applications.
  • Join a collaborative environment with strong investment in professional development and continuous learning.

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