Senior Risk Analytics Consultant - SAS, Fraud Risk Strategy, Credit Cards

In this role, you will:

• Lead or participate in moderately complex initiatives and delivering insight and decision strategies within analytics and reporting and contribute to large-scale planning related to Risk Analytics.

• Review and analyze moderately complex data aggregation, forecasting, reporting, and programming models.

• Be responsible for process production, data reconciliation, and model documentation in alignment with policy governance.

• Resolve data, production, business modeling, and lead team to meet Risk Analytics deliverables while leveraging solid understanding of risk reporting policies, modeling strategies, procedures, regulatory requests, and compliance requirements.

• Collaborate and consult with peers, experienced managers, compliance, and technology to resolve modeling, forecasting, and production issues, and achieve analytic and reporting goals.

• Lead projects, teams, and mentor less experienced staff.

• Partner with cross enterprise risk analysts in development of common modeling strategies.

Required Qualifications:

4+ years of Risk Analytics experience, or equivalent demonstrated through one or a combination of the following: work experience, training, military experience, education.

Desired Qualifications:

• The incumbent would functionally contribute as an SME to the Consumer Lending Credit Cards Fraud Risk team and execute relatively large and complex projects with moderate to high risk for the line of business.

• Leads significant initiatives and processes, and partners with line of business management to drive the company risk management culture and business performance with inputs from senior leadership. Acts as a subject matter expert for senior leaders.

• Assesses and predicts risk and performance through business analysis and/or modeling.  Establishes effective policies, processes, and tools to identify and manage risks. Uses predictive sciences for developing future ready solutions.

• Leads or assists in development of predictive strategies / solutions across customer life cycle, for example new account acquisitions pre booking strategies, post booking third party & first party fraud strategies, payment fraud strategies etc. Analyzes big data and understand / monitor trends to provide actionable insights across a range of risk analytics initiatives.

• Develops comprehensive monitoring frameworks and dashboards and provide statistically sound diagnostic evaluation of any emerging or unexpected risk areas to enhance intelligence and facilitate faster decision making.

• Develop new and enhance existing models/prototypes for managing fraud risk, payment risk, credit bust outs etc. Evaluate new data sources and attributes, internal and external from extensive case reviews for efficacy in models.

• Develop complex programming models to extract data and/or manipulate databases such as ORACLE, Teradata.

• Ensures resolution of matters requiring attention (MRA) from outside regulators, Audit, Corporate Model Risk or internal review teams. May partner with other business units, Audit, Legal, regulators, and industry partners on risk related topics.

• To be effective in this position, you will need to have a good understanding of credit/fraud, practice in the test and learn discipline and exploratory analysis and be fluent in both the key technical tools of credit risk decisioning and sound credit/fraud judgment.

• Bachelor’s degree or higher in a quantitative field such as applied mathematics, statistics, engineering, finance, economics, econometrics or computer sciences

• 4+ years of progressive experience in credit and risk analytics roles

• Hands on Experience in at least one area in credit risk analytics - credit strategy / modeling techniques / Forecasting techniques / data architecture & management

• Detailed understanding of risk domain; Strong Risk analytics skills and understanding of P&L, drivers

• Expertise in few programming and statistical packages - SAS, SQL, Macros, R, Python, E-miner, Tableau

• Knowledge of advanced statistical tools such as Segmentation Tools, Decision Trees, Clustering, Regression, and other statistical modeling and/or Machine Learning techniques

• Ability to lead projects and coordinate with multiple stakeholders.

• Strong analytical skills with ability to turn findings into executable plans to meet business objectives.

• Ability to identify and evaluate trends, isolate root cause, and provide swift/thorough resolution. 

Job Expectations:

• Advanced degree in statistics/finance /engineering/economics/other quantitative disciplines.

• Knowledge and understanding of (consumer lending) credit card lending practices, policies, procedures, and key drivers impacting credit offerings.

• Ability to contribute to strategic decisions and coordinate with multiple stakeholders. Develop consensus and gain buy-in for strategic priorities.

• Strong project management skills with ability to prioritize work, meet deadlines, achieve goals, and work under pressure in a dynamic and complex environment.

• Strong ability to develop partnerships and collaborate with other business and functional area.

• Shift Timings: 1:30 to 10:30 PM