Python & SQL Business Analytics Analyst

Citi Analytics & Information Management (AIM) team is a global community that objectively connects and analyzes information, to create actionable intelligence for our business leaders. The Anti Money Laundering (AML) Data Science & Model Management (DSMM) analyst will be a part of AIM, based in Bangalore and reporting into the VP leading the team.

The scope of work includes all aspects of analysis performed by the team within different projects: Threshold Tuning, Segmentation and data modeling/validation efforts depending on current needs and project plans. A primary area of focus for this position will be working on threshold tuning for Optimization, developing Logistic Regression Model to predict customer behavior, identifying anomalies in transaction and Customer behavior, Outlier detection, ATL threshold tuning, Segment customers into homogenous groups using clustering, Logistic Regression Model Performance Review while maintaining flexibility to switch amongst work streams based on business needs.

The DSMM statistician will follow the globally consistent methodology but is expected to have a high level of initiative and creativity and suggest enhancements to the current methodologies. The role requires working closely with business partners based in other geographies that Citi operates in (e.g., NAM, APAC, and LATAM).

Requirements include a background in analysis using databases, warehouses, data processing, experience with statistics and data mining. Experience and knowledge in banking and finance, especially in the AML area will be a plus. In addition, the ability to read and create formal documentation is highly desirable.

Responsibilities:

• A primary area of focus for this position will be working on threshold tuning for Optimization, developing Logistic Regression Model to predict customer behavior, identifying anomalies in transaction and Customer behavior, Outlier detection, ATL threshold tuning, Segment customers into homogenous groups using clustering, Logistic Regression Model Performance Review etc. while maintaining the flexibility to switch amongst work streams based on business needs. Apply quantitative and qualitative data analysis methods; prepare statistical and non-statistical data exploration and advanced statistical analysis to support the threshold tuning or segmentation work streams.

• Validate data, identify data quality issues (if any), and work with Technology to address them. Analyze and interpret data reports, draw conclusions and make recommendations answering specific business needs.