Provide analytical/quantitative input to develop, document, implement and monitor the build of complex consumer fraud risk models
Use business analysis, process, and quantitative knowledge to ensure business intent is matched with modeling outcomes
Document development decisions under SR11-7 guidelines
Work on ad-hoc projects as needed
Serve as a key contributor and lead developer for various models (XGBoost, LightGBM, Logistic Regression, GLM)
Work within SYF’s cloud-based data and modeling platforms and tools to develop and refine models and support fraud strategies
Perform in depth analysis on large data sets, and prepare analysis and reports to support discussions on key analytics and model risks
Support development, documentation, implementation and monitoring of true-name fraud, synthetic risk, and merchant risk models using SAS/Python
Work closely within the credit and risk organization to validate accuracy and performance of statistical models and to identify issues requiring further investigation
Assist in development/understanding of vendor models to ensure accuracy and relevancy
Provide independent research and analysis to support conceptual soundness of key models
Liaise with the Synchrony Financial business teams to uncover and highlight model risk associated with models
Keep pace with the latest developments in academia, regulatory environment, risk technology (vendor and in-house) and financial services industries to embrace change and drive improvements cross-functionally.

Requirements

Bachelor’s Degree (Masters preferred) in Mathematics/Statistics, Operations Research, Economics, Finance or other quantitative discipline
2+ years of experience in Consumer Lending statistical modeling/risk analytics, preferably related to credit cards, identity fraud and true name fraud
2+ years in coding with SAS, Python, R or other equivalent tool within the recent 5 years
Proficiency in writing SQL queries for data analysis, automation, and reporting purposes
Minimum of 2 years of experience working with data processing and analysis tools and packages such as Hive, PySpark, Numpy Pandas, dplyr, Sparklyr, SparkR, Tidyverse, Lubridate or equivalent libraries
Strong ability to manipulate, transform, and analyze large datasets efficiently
Strong written/oral, project management skills, communication skills with the ability to manage multiple assignments effectively
Ability and flexibility to travel for business as required

Benefits

Competitive salary
Annual bonus based on individual and company performance

Applicant Tracking System Keywords

Tip: use these terms in your resume and cover letter to boost ATS matches.

Hard Skills & Tools

statistical modelingrisk analyticsdata analysisSQLSASPythonRXGBoostLightGBMLogistic Regression

Soft Skills

project managementcommunicationanalytical thinkingproblem solvingcollaborationflexibilityindependent researchattention to detailtime managementinterpersonal skills