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Tier-1 brand and metro location increase competition, but senior ML/MLOps specialization limits candidate pool.
Preference for banking experience, regulatory compliance, and model governance makes industry background highly relevant.
Explicit 8-12 years plus mandatory Python/SQL and ML stack requirements indicate a high filtering bar.
Lead design and execution of complex data analysis and AI/ML projects involving large structured and unstructured datasets.
Manage quality, volume, timeliness, and resource planning of data science deliverables impacting business strategy and risk management.
Drive production deployment, monitoring, and governance of machine learning models while influencing strategic decisions with AI-powered insights.
8-12 years of experience in Data Analytics, Data Science, or Advanced Analytics roles.
Strong programming skills in Python (required); experience with PySpark preferred.
Proficiency in SQL and relational databases; hands-on experience building and deploying machine learning models using libraries like scikit-learn, XGBoost, TensorFlow, or PyTorch.
Bachelor's degree or equivalent required; Master’s degree or specialization in AI/ML/Data Science preferred.
Experienced in working with large-scale enterprise or financial datasets, preferably in banking or financial services.
Skilled at translating complex AI/ML insights for non-technical stakeholders and managing multiple priorities in regulated environments.
Proficient with MLOps practices including model deployment, monitoring, and operationalization to ensure scalable AI-driven solutions.