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Tier-1 brand, mid-level seniority, and popular data scientist title increase applicant competition.
Technical ML skills transfer across industries, but financial-data domain preference raises moderate sensitivity.
Explicit 6–8 years plus mandatory ML/LLM, MLOps, cloud, and production experience raises filtering rigor.
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Own the product vision and execution for a data management framework handling data acquisition, cleaning, transformation, and workflows.
Lead end-to-end AI/ML lifecycle activities including design, experimentation, deployment, and continuous improvement of production-grade AI models.
Collaborate with analytics domain experts and engineering teams to develop integrated financial analytics data pipelines and maintain forward-looking solution strategies.
6–8 years experience in data science, analytics, or statistical modeling roles.
Master’s degree in Statistics, Mathematics, Computer Science with Data Science certification, or Engineering degree specializing in Data Science and AI.
Strong expertise in Python, ML/DL libraries (TensorFlow, PyTorch, Scikit-learn), NLP, Large Language Models, prompt engineering, and RAG workflows.
Experience with cloud platforms (AWS or Azure), Git-based version control, CI/CD pipelines, and deploying AI/ML solutions in production environments.
Experienced in financial services or investment banking environments with familiarity of financial data workflows or LSEG/Refinitiv products.
Demonstrated ability to lead product vision and strategy translating complex analytics needs into practical, scalable AI and data management solutions.
Skilled in cross-functional collaboration with both technical and senior business stakeholders to drive AI/ML initiatives end-to-end and align with strategic objectives.