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Tier-1 brand plus a mid-level Bengaluru data scientist role with broad skills increases candidate competition.
Core data science skills transfer well, but financial markets and Refinitiv familiarity add domain specificity.
Explicit 3–5 years plus mandatory ML/NLP, cloud and MLOps skills enforce strict shortlisting.
Design and implement enterprise-grade data management and financial analytics workflows integrating multiple tools and services.
Build, train, validate, and deploy machine learning and deep learning models (including NLP, LLMs, RAG) on cloud platforms using MLOps and CI/CD best practices.
Collaborate with analytics domain experts, engineering, and project teams to deliver scalable data and AI solutions aligned with organizational goals and communicate outcomes to stakeholders.
3–5 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.
Proficiency in Python and data science libraries; experience with deep learning frameworks (TensorFlow or PyTorch); familiarity with relational databases, statistics, Git, CI/CD, and cloud environments (AWS/Azure).
Experience building machine learning models including NLP, LLMs, RAG, and applying web scraping and data preprocessing techniques.
Experienced in financial analytics data pipelines or financial services domain, preferably with knowledge of Refinitiv or LSEG datasets.
Capable of working on distributed/cross-location teams with strong technical insight to support strategy execution.
Able to translate complex business data requirements into scalable technical solutions with strong analytical and communication skills suitable for technical and non-technical audiences.