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.
Responsibilities- 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.
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 [email protected]. 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.
Skills Required
- Master's degree in Business, Engineering, Mathematics, or a related field, plus five years of professional data analytics experience; alternatively, a bachelor's degree plus seven years of experience.
- Five or seven years of experience working with complex data structures, large financial datasets, and credit bureau data, depending on the degree path.
- Five or seven years of experience with SAS, SQL, Advanced Excel, VBA, PowerPoint, Visio, Qlik, Sisense, R, and Tableau, depending on the degree path.
- Three years of experience in risk management within consumer banking, financial services, and lending.
- Three years of experience using logistic regression, cohort analysis, customer lifetime value, clustering, and/or market mix modeling.
- Three years of experience developing and optimizing underwriting policies for commercial or consumer lending.
- Three years of experience developing machine-learning models, including Gradient Boosting models.
- Three years of experience designing business experiments and A/B tests using statistical methods.
- Three years of experience with model-performance monitoring metrics, including R-squared, sensitivity, correlation, rank ordering, Gini coefficient, KS statistics, and/or investment ROI.
- Three years of data analytics experience in risk management within banking, financial, or insurance industries.
- Two years of experience leading a team of direct reports.
What We Do
Choosing a digital partner is about more than capabilities — it’s about collaboration and character. Unrealistic overhauls and off-the-shelf products ignore what matters most — your unique needs, culture, goals, and your legacy data and technology environments. At EXL, our collaboration is built on ongoing listening and learning to adapt our methodologies. We’re your business evolution partner—tailoring solutions that make the most of data to make better business decisions and drive more intelligence into your increasingly digital operations. Whether your goals are scaling the use of AI and digital, redesign operating models, or driving better and faster decisions, we’re here to partner with you to help you gain—and maintain—competitive advantage with efficient, sustainable models at scale. Our expertise in transformation, data science, and change management helps make your business more efficient and effective, improve customer relationships and enhance revenue growth. Instead of focusing on multi-year, resource- and time-intensive platform designs or migrations, we look deeper at your entire value chain to integrate strategies with impact. We use our specialization in analytics, digital interventions, and operations management—alongside deep industry expertise — to deliver solutions that help you outperform the competition. At EXL, it’s all about outcomes—your outcomes—and delivering success on your terms. Share your goals with us and together, we’ll optimize how you leverage data to drive your business forward. For more information, visit www.exlservice.com.
