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Tier-1 brand, mid-level ML role, metro location and popular Data Scientist title increase applicant competition.
Core ML/LLM skills are transferable, but finance domain and Refinitiv familiarity raise fit sensitivity moderately.
Mandatory 6–8 years plus specific ML/LLM, cloud, and productionization skills create strict shortlisting filters.
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Own the product vision and execution for a data management framework covering data acquisition, cleaning, transformation, and workflows in financial analytics.
Support end-to-end AI/ML lifecycle including design, experimentation, deployment, and continuous improvement of production-grade AI models aligned with business strategy.
Collaborate with domain experts, engineering, and project teams to deliver integrated financial analytics data pipelines and scalable AI-driven solutions.
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 degree specializing in Data Science/AI.
Strong expertise in data analytics, NLP, deep learning, Large Language Models (LLMs), RAG workflows, Python, and ML/DL libraries (TensorFlow, PyTorch, Scikit-learn).
Experience with deploying AI/ML solutions on cloud platforms (AWS/Azure), Git-based version control, CI/CD pipelines, and data management tools.
Experienced in financial services or investment banking environments, familiar with financial data workflows (preferably Refinitiv or LSEG products).
Able to lead cross-functional delivery, communicate complex AI/ML concepts clearly to technical and business stakeholders, and influence product strategy.
Hands-on practitioner fluent in NLP, deep learning, prompt engineering, RAG, and cloud deployment with strong coding standards and MLOps knowledge.