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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 focus on threshold tuning for Optimization, developing Logistic Regression models, identifying anomalies in transactions, and customer behavior while working closely with global partners.
Job Responsibility:
Work 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
Requirements:
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
Nice to have:
Knowledge in SAS is preferred but not mandatory
VBA experience is a plus
Provide input into the innovation of new and enhanced approaches. Having initiative and a proactive attitude is desirable
What we offer:
Best-in-class benefits to ensure employees are well, live well, and save well
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