Senior Data Scientist
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Key skills for this role
About the Role
Senior Data Scientist – Corporate Banking We are working with a leading financial institution that is looking to hire a Senior Data Scientist to join its growing Data & AI function.
Key Skills for This Role
Full Job Posting
Senior Data Scientist – Corporate Banking
We are working with a leading financial institution that is looking to hire a
Senior Data Scientist
to join its growing Data & AI function.
This role requires a combination of advanced data science expertise and deep Corporate Banking domain knowledge.
Candidates must have experience working within Corporate Banking, Corporate & Investment Banking (CIB), Commercial Banking, CIBG, or CCIB environments.
Key Requirements
- Minimum 7 years of experience in Data Science, with proven expertise in:
- Artificial Intelligence (AI) and Machine Learning (ML)
- Statistical Modelling and Predictive Analytics
- Generative AI and Machine Learning Operations (MLOps)
- Cloud-based data science and model deployments
- Strong understanding of banking processes, operations, products, and industry terminology.
- Experience working with Corporate Banking products such as: Treasury,Trade Finance and Foreign Exchange (FX)
• Corporate Cash Management
- Proven track record of delivering scalable, production-grade AI and analytics solutions within regulated, data-driven environments.
- Experience leading cross-functional teams, agile squads, and complex transformation initiatives.
Key Responsibilities
- Develop and deploy advanced AI, machine learning, and statistical models to support business growth and operational excellence.
- Design and implement Generative AI solutions to enhance customer experience, operational efficiency, and decision-making.
- Establish and maintain MLOps frameworks to ensure reliable, scalable, and governed model deployment.
- Partner with business stakeholders across Corporate Banking functions to identify high value analytics opportunities.
- Lead agile delivery teams through the full data science lifecycle, from problem definition through to production deployment.
- Work closely with Data Engineering, Architecture, and Technology teams to build robust and scalable solutions.
- Ensure compliance with governance, risk, and regulatory requirements while maintaining best practices in model development and monitoring.
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