Junior Data Scientist
Skills
About This Role
Job Purpose
Support delivery of ML + GenAI (LLM) use cases in a regulated banking environment, with primary focus on model documentation, AI governance, and Model Risk Management (MRM) readiness.
Key Responsibilities
- Work with large and complex data sets to solve challenging business problems
- Support development and controlled updates of ML/GenAI components mainly to ensure reproducibility, versioning, and governed release.
- Document evaluation results for ML/LLM components (test cases, thresholds, failure modes, mitigations) to enable governance review and validation readiness.
- Produce and maintain audit-ready model documentation (purpose, scope, data lineage/governance, methodology, assumptions, limitations, evaluation evidence, monitoring plan, change log).
- Support MRM governance deliverables: Model Charter inputs, Model Inventory updates, and AI risk assessment/tiering evidence packs (including Human‑in‑the‑Loop aspects where required).
- Document evaluation results for ML/LLM components (test cases, thresholds, failure modes, mitigations) to enable governance review and validation readiness.
Professional Experience/Qualifications
- Experience: 2–5 years (Data Science AI/ML; banking preferred)
- Python (pandas/NumPy/scikit‑learn) and SQL; strong data preparation + EDA foundations.
- Solid understanding of ML fundamentals and model evaluation concepts.
- Working knowledge of GenAI/LLM basics (prompting/RAG concepts) and ability to document risks/controls clearly.
- Demonstrated exposure to model documentation/governance/MRM in a regulated or enterprise setting (charter, inventory, risk assessment, monitoring documentation).
- Strong written communication: ability to turn technical work into crisp, structured documentation
- Experience deploying models on AWS, Google Cloud, Azure, or similar (e.g., Sagemaker, Vertex AI)
- Educational qualifications: Bachelor’s degree in computer science, Engineering, Data Science/Statistics or related field.
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