Job Description

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 the 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., U.S., APAC, and EMEA).


Requirements include a background in analysis using data bases, 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. Automate data extraction and data preprocessing tasks. Perform ad hoc data analyses. Design and maintain complex data manipulation processes. Provide consistent documentation and presentations. Develop new transaction monitoring scenarios based on emerging Financial Crime Risk Document solutions and present results in a simple comprehensive way to non-technical audience, as well as write more formal documentation using statistical vocabulary. Generate new ideas, concepts and models to improve methods of obtaining and evaluating quantitative and qualitative data. Identify relationships and trends in data, as well as any factors that could affect the results of research. Question and validate assumptions. Escalate identified risks and sensitive areas in terms of methodology and processes.

Qualifications:

1-2 years of experience in Financial Services / Analytics Industry Masters in a numerate subject such as Mathematics, Operational Research, Business Administration, Economics etc. from Premier Institute or a track record of performance that demonstrates this ability. Previous experience with financial services companies (retail banking, small business banking, commercial, institutional, private banking) Experience in 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 the flexibility to switch amongst work streams based on business needs Good Knowledge in Python, SQL, Hive. Knowledge in SAS is preferred but not mandatory Strong statistics and data analytics academic background and knowledge of quantitative methods Highly skilled and good knowledge of MS Excel. VBA experience is a plus Experience in reporting the results of analysis in clear written form, and in presenting the findings during meetings and conference calls Team working experience (demonstrated team player ability required) Understanding of technical requirements, ability to communicate with Technical Support Very good written and verbal communication ability in an educational style. Ability to express thoughts and concepts clearly. Ability to work well with a variety of people and to show team-player attitude regardless the scope of responsibilities. Provide input into the innovation of new and enhanced approaches. Having initiative and a proactive attitude is desirable

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Job Family Group:

Decision Management
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Job Family:

Business Analysis
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Time Type:

Full time
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Most Relevant Skills

Please see the requirements listed above.
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Other Relevant Skills

For complementary skills, please see above and/or contact the recruiter.
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