Engagement Manager

Engage with clients to identify key business problems and improvement opportunities. Provide day to-day project management and execution of analytics products and services. Use statistical software and tools like SAS, SQL, Advanced Excel, VBA, Cart, R, R Shiny and Tableau to perform risk analytics. Develop, validate, and monitor statistical models to help clients manage financial risk and external stress due to macroeconomic events. Use analytics to optimize risk capital allocation and improve exposure management strategies.

Requires Master's degree in Business, Engineering, Mathematics, or a related field plus Five (5) years of Professional data analytics experience.

Experience must include: Five (5) years of experience with the following:

(1) working with complex data structures, large financial datasets and credit bureau data; and

(2) SAS, SQL, Advanced Excel, VBA, PPT, Visio, Qlik, Sisense, R, and Tableau software; Three (3) years of experience with the following: (1) risk management in consumer banking/financial services and lending; (2) working with complex data structures, large financial datasets, and credit bureau data

(3) key modeling and analytical techniques, including logistic regression, cohort analysis, customer lifetime value, clustering methodologies and/or market mix modeling

(4) using analytics to develop and optimize underwriting policies for commercial/consumer lending

(5) developing Machine Learning models including Gradient Boosting models

(6) designing business experiments and A/B tests using statistical methods

(7) model performance monitoring metrics, including Rsquare, Sensitivity, Correlation, Rank Ordering, Gini coefficient, KS statistics and/or Investment ROI and

(8) Data analytics experience in risk management in banking, financial or insurance industry; Two (2) years of experience leading a team of direct reports.

Alternatively, the employer also accept a Bachelor's degree in Business, Engineering, Mathematics, or a related field plus seven (7) years of Professional data analytics experience in lieu of a Master's degree plus Five (5) years of described experience.

Experience must include: Seven (7) years of experience with the following:

(1) working with complex data structures, large financial datasets and credit bureau data; and

(2) SAS, SQL, Advanced Excel, VBA, PPT, Visio, Qlik, Sisense, R, and Tableau software; Three (3) years of experience with the following: (1) risk management in consumer banking/financial services and lending; (2) working with complex data structures, large financial datasets, and credit bureau data

(3) key modeling and analytical techniques, including logistic regression, cohort analysis, customer lifetime value, clustering methodologies and/or market mix modeling

(4) using analytics to develop and optimize underwriting policies for commercial/consumer lending

(5) developing Machine Learning models including Gradient Boosting models

(6) designing business experiments and A/B tests using statistical methods

(7) model performance monitoring metrics, including Rsquare, Sensitivity, Correlation, Rank Ordering, Gini coefficient, KS statistics and/or Investment ROI and

(8) Data analytics experience in risk management in banking, financial or insurance industry; Two (2) years of experience leading a team of direct reports.

40 hours/week, 9:00am-5:00pm, Salary range: $183,000 to $190,000 per year.

To apply: Send resume and cover letter to us.careers@exlservice.com. Must cite job title and code EXL95 in response. This notice is subject to ExlService.com, LLC''s employee referral program. EEO/Minorities/Females/Vets/Disabilities.

• Develop machine learning models using Python/R/SAS to create risk/fraud scores used to underwrite commercial and consumer credit applications.
• Create statistically sound experiments to improve client profitability and risk policies using design of experiments (DOE) methods.
• Develop automated dashboards to monitor key performance indicators using Visual Basic and Tableau.
• Create, manage, and manipulate analytical datasets using big data querying tools including Teradata, Hive, and MySQL.
• Use forecasting methods to predict future financial performance of portfolios using historical and current trends.
• Design risk management strategies across customer credit lifecycle to improve portfolio profitability and protection against future recessions.
• Lead performance appraisal of reporting team members.
• Work closely with senior client management to execute analytical solutions, meet regulatory goals and outline long/short term strategy roadmaps.
• Position may work at various and unanticipated worksites throughout the United States. Telecommuting permitted.
• Develop machine learning models using Python/R/SAS to create risk/fraud scores used to underwrite commercial and consumer credit applications.
• Create statistically sound experiments to improve client profitability and risk policies using design of experiments (DOE) methods.
• Develop automated dashboards to monitor key performance indicators using Visual Basic and Tableau.
• Create, manage, and manipulate analytical datasets using big data querying tools including Teradata, Hive, and MySQL.
• Use forecasting methods to predict future financial performance of portfolios using historical and current trends.
• Design risk management strategies across customer credit lifecycle to improve portfolio profitability and protection against future recessions.
• Lead performance appraisal of reporting team members.
• Work closely with senior client management to execute analytical solutions, meet regulatory goals and outline long/short term strategy roadmaps.
• Position may work at various and unanticipated worksites throughout the United States. Telecommuting permitted.