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Tier-1 brand, popular Data Scientist title, and Bangalore metro increase candidate competition density.
Core ML/NLP skills transferable, but financial-data experience and Refinitiv familiarity increase sensitivity.
Explicit 6–8 years plus mandatory ML, NLP, cloud, and MLOps skills raise filtering strictness.
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Own the product vision and strategy for a financial data management framework encompassing data acquisition, cleaning, transformation, and workflows.
Lead end-to-end AI/ML lifecycle activities, including design, experimentation, deployment, and optimization of production-grade AI models for financial analytics.
Collaborate closely with analytics experts, engineering, and multiple stakeholders to deliver scalable, integrated, data-driven solutions aligned with business needs and market trends.
6–8 years of experience in data science, analytics, or statistical modelling roles.
Master’s degree in Statistics, Mathematics, Computer Science with Data Science certification, or Engineering specializing in Data Science/AI.
Strong expertise in Python, NLP, deep learning (TensorFlow, PyTorch), predictive modelling, LLMs, RAG workflows, and data processing of diverse data types.
Experience with cloud platforms (AWS/Azure), MLOps, Git-based version control, CI/CD pipelines, and commercial or open-source data management tools.
Experienced in designing and leading AI/ML initiatives within financial services or investment banking environments with familiarity of financial data workflows.
Capable of bridging technical AI model development with business objectives and senior stakeholder communications, including cross-functional and global teams.
Hands-on expert in advanced NLP methods (including LLMs, prompt engineering), managing end-to-end ML lifecycle and deploying scalable AI solutions in production.