Data Scientist

You will work across functional teams to deliver intelligent solutions for financial use cases like credit risk, fraud detection, and personalisation, while mentoring junior data scientists and ensuring adherence to enterprise governance and architecture standards.

• Design, develop, test, and deploy production-grade ML, AI, generative AI, and agentic AI models.
• Create reusable AI blueprints for multiple use cases, ensuring scalability and performance.
• Develop guardrails to align with enterprise security, architecture, and governance standards.
• Collaborate with stakeholders across Business Units (e.g., Risk, Compliance) to identify, design, and deploy impactful solutions.
• Translate complex business problems into structured tasks, delivering measurable value through deployed models.
• Build and maintain model pipelines using enterprise MLOps frameworks for audibility and scalability.
• Monitor and optimize model performance, re-training as needed in dynamic banking environments.
• Mentor junior data scientists on model selection, experimentation protocols, and deployment best practices.
• Stay updated on advancements in foundation models, multi-agent systems, and AI governance frameworks.

• Bachelor’s or Master’s degree in Computer Science, Data Science, Statistics, Engineering, or a related field (PhD a plus).
• 3–5+ years of experience in applied data science or AI roles, preferably in financial services or banking.
• Proficiency in Python and data science toolkits (e.g., Pandas, NumPy, Scikit-learn, TensorFlow, PyTorch).
• Experience with platforms like Databricks, AWS Bedrock, or Amazon SageMaker for AI/ML development.
• Hands-on experience with vector databases (e.g., OpenSearch, Pinecone, FAISS, Milvus) and AI orchestration frameworks (e.g., LangChain, LlamaIndex).
• Strong understanding of MLOps tools (e.g., MLflow, Kubeflow, Docker, Airflow) and cloud platforms.
• Experience deploying models in regulated environments with a focus on model risk, explainability, and compliance.
• Familiarity with financial use cases such as credit scoring, fraud detection, KYC, and personalization.
• Excellent communication skills to explain technical concepts to non-technical stakeholders.
• Ability to manage competing priorities in a fast-paced, business-critical environment.

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