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Tier-1 bank, metro Bangalore, mid-level generalist analytics with common Python/SQL skills increases competition.
Strong AML and banking experience preference reduces cross-industry transferability.
Explicit 2-4 years plus mandatory Python/SQL/Hive and AML knowledge makes screening moderately strict.
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Responsible for threshold tuning for optimization, developing and reviewing logistic regression models to predict customer behavior, and conducting anomaly and outlier detection.
Segment customers into homogeneous groups using clustering and validate data quality, working closely with technology teams to resolve issues.
Analyze and interpret data to support AML-related projects, automate data extraction and preprocessing, and present findings to both technical and non-technical audiences.
2-4 years of experience in Financial Services or Analytics Industry.
Master’s degree in Mathematics, Operational Research, Business Administration, Economics, or related numerate subject from a premier institute or equivalent performance track record.
Proficiency in Python, SQL, and Hive; strong statistics and data analytics knowledge; experience with logistic regression, threshold tuning, clustering, and anomaly detection.
Good written and verbal communication skills; ability to document and present analysis results effectively.
Experienced in AML data science or financial crime risk analytics with exposure to banking and finance domain.
Able to handle multiple analytic work streams flexibly, including model development, tuning, and performance review.
Capable of collaborating with global stakeholders and translating complex statistical concepts into clear documentation and presentations.