Assistant Vice President - Data Engineering - Sales Practice Effectiveness

Position: AVP(Data Engineering )

Location: Chennai

Duration: Fulltime

Experience: 8-12 Years

Description

Role Outline/Job Summary:

- The role of Sales Practice Effectiveness (SPE) Analyst/Data Engineer is part of Global Decision Management (GDM), based in Bangalore and reporting into the VP/SVP leading the team

- The scope of work includes all aspects of analysis performed by the team within different projects: Segmentation, Entity Linkages, Pattern Mining and Anomaly Detection and data modeling/validation efforts depending on current needs and project plans

- A primary area of focus for this position will be working on Data Manipulation and Pattern recognition, and some aspects of Segmentation modeling, while maintaining the flexibility to switch amongst work streams based on business needs

Role & Responsibilities:

Business/Department Objectives:

- The scope of work includes all aspects of analysis performed by the team within different projects: Segmentation, Entity Linkages, Pattern Mining and Anomaly Detection and data modeling/validation efforts depending on current needs and project plans

- The Lead Analyst (AVP) 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, EMEA)

- Requirements include a background in analysis using data bases, warehouses, data processing; experience with statistics and data mining with a strong background on Programming in R/Python and Big Data Technologies. Experience and knowledge in banking and finance especially in the AML/Fraud area will be a plus

Core Responsibilities:

- Lead Solution development in the area of Sales Practice effectiveness - Provide thought leadership on solution framework, data manipulation algorithm design

- 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

- Conduct data-driven analysis. Apply quantitative and qualitative data analysis methods; prepare statistical and non-statistical data exploration and advanced statistical analysis to support the pattern mining or entity linkages for Sales Malpractices or Fraud

- 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

- Document solutions and present results in a simple comprehensive way to non-technical audience, as well as write more formal documentation using statistical vocabulary

- Industrialization of Solutions developed in the area across markets - Maintain an industry leading knowledge of the tools, systems and processes available for best in class Fraud or Anomaly Detection