Reference 26000FXD

Responsibilities

Lead ML Analyst – Wholesale Banking

Role Summary

The Lead ML Analyst is a senior individual contributor responsible for connecting Wholesale Banking business needs with Data Science, Machine Learning, and Generative AI solutions.

The role drives business engagement, requirement gathering, prioritization, capacity planning, and successful adoption of AI/ML initiatives while working closely with business stakeholders, Data Scientists, and ML Engineers.

Mandatory Skills

Strong communication, stakeholder management, and business consulting skills.
Ability to gather, structure, and translate business requirements into AI/ML use cases.
Deep understanding of one or more Wholesale Banking domains with end-to-end process knowledge.
Expertise in demand management, prioritization, capacity planning, and project coordination.
Ability to understand the digital strategies of organizations and objectives of the group/business lines, align the solutions accordingly.
Good understanding of LLM architectures, GenAI concepts, and prompt engineering techniques.




Strong data visualization, storytelling, and insight-generation capabilities.

Additional Skills

Working knowledge of Python and common data science libraries.
Good understanding of Machine Learning concepts and practical applications.
Familiarity with SDLC, CI/CD pipelines, APIs, and deployment practices.
Knowledge on Streamlit, dashboards, and user-facing analytical applications.
Valuable understanding of different Retrieval-Augmented Generation (RAG) architectures.

Optional Knowledge

Exposure to Kubernetes and Airflow.
Familiarity with Azure AI services and cloud-based analytics platforms.
Awareness of Agentic AI frameworks such as LangChain and LangGraph.

Understanding of MLOps and production AI/ML deployment practices.

Additional Skills

Profile required

Working knowledge of Python and common data science libraries.
Good understanding of Machine Learning concepts and practical app

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