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Tier-1 brand, mid-level ML role, popular skillset and metro hiring make competition high.
Requires banking risk/control domain expertise and ML/LLM specialization, limiting cross-industry transferability.
Explicit 5–8 years plus mandatory ML/LLM frameworks and banking domain expertise makes shortlisting strict.
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Design, develop, and implement AI-powered solutions using large language models (LLMs) and machine learning to address business challenges and improve controls.
Collaborate with cross-functional teams to integrate AI and LLM technologies into existing products and workflows, and provide performance analysis and continuous improvements.
Manage data governance and ensure data quality in partnership with data engineers, while acting as a subject matter expert on data science methodologies and best practices.
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 focusing on AI, LLMs, and prompt engineering.
Proficiency in Python and relevant libraries (Numpy, Pandas, Scikit-learn), experience with ML frameworks (TensorFlow, PyTorch, OpenAI, LangChain), and big data platforms (Hadoop, Spark) plus SQL databases.
Strong understanding and hands-on experience with LLMs, AI tools, prompt engineering, and ability to develop scalable machine learning models.
Experienced in banking domain areas like Wealth, Cards, Deposits, Loans, and Insurance with knowledge of business risk, controls, and compliance.
Operates effectively in a fast-paced, dynamic environment requiring collaboration across model risk management, engineering, analytics, and product teams.
Demonstrates strong communication skills for technical and non-technical stakeholders alongside ability to mentor junior team members and lead organizational initiatives.