Description

You will apply statistical analysis and machine learning to detect financial crime patterns and optimize transaction monitoring within the banking sector.

This role is on-site.

Responsibilities

• Develop and validate logistic regression models to predict customer behavior and identify transaction anomalies.
• Tune monitoring thresholds to optimize detection rates while minimizing false positives.
• Automate data extraction, preprocessing, and complex data manipulation pipelines using Python and SQL.
• Analyze quantitative and qualitative data to identify trends, validate data quality, and present findings to non-technical stakeholders.
• Design new transaction monitoring scenarios based on emerging financial crime risks and regulatory requirements.

Required Skills

• 5+ years of experience in data analytics, preferably within Financial Services or Anti-Money Laundering (AML).
• Strong proficiency in Python and SQL for data mining, statistical analysis, and machine learning.
• Hands-on experience with Logistic Regression, anomaly detection, and clustering techniques.
• Deep understanding of statistical methods, quantitative analysis, and data validation.
• Experience with Hive, VBA, and MS Excel for data processing and reporting.
• Ability to translate complex analytical results into clear, actionable business recommendations.

Preferred Skills

• Experience in prompt engineering and generative AI applications.
• Knowledge of ATL threshold tuning and segmenting customers into homogeneous groups.
• Strong background in banking, finance, and AML regulatory frameworks.

Referral Bonus: ₹50,000

Key Skills

Education

Any Graduate