Data Scientist – Corporate & Institutional Banking

Responsibilities

• Use Python or R to explore data, perform analysis, and rapidly prototype analytical approaches within repeatable workflows.
• Design, develop, validate, and monitor interpretable machine learning models using sound statistical and modeling techniques.
• Own the end‑to‑end delivery of analytical solutions—from prototype through testing, validation, and scalable production deployment—collaborating closely with engineering and testing partners to ensure production readiness.
• Act as a key communication bridge across business, product, and engineering teams to gather and document requirements, clearly communicate analytical solutions, and ensure business needs are accurately implemented.
• Define and track performance metrics to measure solution effectiveness and business impact.

• 2–3 years of relevant, post‑graduate professional experience as a Data Scientist or in a comparable analytics role.
• Ability to design and develop interactive dashboards to communicate, visualize, and monitor analytical results using Python or R–based frameworks (e.g. R Shiny, Dash, Flask)
• Strong programming experience in Python or R.
• Strong SQL skills and experience working with large datasets.
• Experience working with Apache Spark using one or more languages (e.g. PySpark, sparklyr, or Spark SQL).
• Experience with Git or comparable version control tools.
• Experience developing and evaluating traditional ML models, including feature engineering and performance assessment.
• Experience building applied GenAI solutions, including familiarity with retrieval augmented generation and related architectural approaches.
• Experience producing delivery artifacts such as business requirements, user stories, and test cases.
• Experience supporting testing, validation, and transitions from analytical prototype to production.
• Exposure to business domains such as credit, accounting, or financial operations.
• Familiarity with underwriting concepts or a willingness to learn them on the job.
• Experience working across multiple business areas or interest in developing cross‑domain expertise.
• Exposure to entity resolution or record‑linkage problems, including matching, deduplication, or linking entities across disparate internal or external data sources.