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Known payments firm, mid-level generalist ML role in Pune increases candidate competition.
Core ML and SQL skills are transferable, but payments and regulatory experience increases domain specificity.
Explicit 5–8 years and mandatory Python/SQL/statistical modeling and finance-grade accuracy imply rigorous filters.
Design, build, and validate statistical and machine learning models for business problems in financial services (Originations, Risk, Fraud, Digital Engagement, Payments).
Develop and maintain analytics models, rebuilding reporting logic as SQL transformations in cloud data warehouses (Snowflake/BigQuery/Redshift/Synapse).
Translate model outputs into business insights and clear recommendations, using AI tools to accelerate analysis and maintain accuracy.
Bachelor's degree in Statistics, Computer Science, IT, Business/Management Information Systems, or related field.
5-8 years of relevant experience in data science or analytics roles.
Strong proficiency in Python (pandas, scikit-learn, statsmodels) and SQL with experience on large datasets; familiarity with Snowflake or similar cloud data warehouses is a plus.
Solid foundation in statistical modeling (regression, classification, hypothesis testing), feature engineering, model validation, and data visualization.
Experienced in handling fragmented and incomplete data across multiple disconnected systems, able to reconcile and synthesize data independently.
Demonstrates an AI-first approach, routinely leveraging AI tools (e.g., Copilot, ChatGPT) to speed analysis with accuracy.
Background or strong interest in financial services domains such as credit risk, lending, payments, or fintech, with cross-domain data integration experience.