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Tier-1 brand, mid-level ML role, metro hiring, and broad AI skillset increase candidate competition.
Strong transferable ML/LLM skills but banking controls and compliance requirements impose moderate domain specificity.
Explicit 5-8 years plus mandatory LLM/ML frameworks and banking controls/domain knowledge creates strict filters.
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Design, develop, and deploy AI-powered solutions using large language models (LLMs) and other machine learning techniques to address business challenges and optimize controls.
Collaborate with cross-functional teams including product managers, engineers, data scientists, and Model Risk Management to identify AI integration opportunities and ensure data quality and governance.
Monitor and analyze AI model performance, perform exploratory data analysis, and implement advanced algorithms to provide actionable insights and continuously improve solutions.
Bachelor’s degree in a quantitative field such as computer science, engineering, mathematics, machine learning, or statistics.
5-8 years of experience in a data science role with expertise in AI, LLMs, and prompt engineering.
Proficiency in Python and related libraries (Numpy, Pandas, Scikit-learn), experience with ML frameworks like TensorFlow, PyTorch, OpenAI, LangChain, and big data platforms (Hadoop, Spark).
Good understanding of banking domains (Wealth, Cards, Deposit, Loans, Insurance) and business risk, controls, compliance, and data management.
Experienced in applying cutting-edge AI and machine learning techniques to financial services risk and control automation.
Comfortable working in a cross-functional, regulated environment involving complex data governance and compliance requirements.
Skilled at translating AI/ML technical capabilities into actionable business insights and driving integration of AI models into enterprise risk management frameworks.