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Tier-1 brand, metro location, broad ML/GenAI skillset and mid-level seniority increase candidate competition.
Core ML skills are transferable across industries, though banking experience preferred, so moderate sensitivity.
Explicit 6–9.5 years plus mandatory GenAI, production ML, and Python/PySpark/SQL requirements make filters strict.
Develop, deploy, and productionize data-driven and machine learning solutions end-to-end, focusing on predictive models such as cross-sell, up-sell, and attrition to optimize customer management and revenue.
Consult with business stakeholders to understand complex challenges and communicate technical outcomes clearly in business-friendly terms.
Identify new opportunities through analysis of structured and unstructured data and continuously improve ML deployment processes including leveraging Generative AI frameworks and prompt engineering.
6 to 9.5 years of professional experience in data science or related roles.
Strong proficiency in machine learning techniques including Decision Trees, Random Forest, and Gradient Boosting for classification and regression tasks.
Programming skills in Python, PySpark, and SQL.
Experience applying Generative AI frameworks (e.g., langchain, langflow, google adk) and prompt engineering skills to enhance business outcomes.
Experienced in end-to-end production deployment of ML models and comfortable handling complex cross-functional stakeholder communication.
Data scientist capable of driving innovation by identifying business opportunities from diverse data sources and improving analytics workflows.
Familiarity with banking domain analytics and modeling is preferable but not mandatory; however, strong problem-solving, accountability, and communication skills are essential.