Build machine learning models for prescreen direct mail campaign targeting; your models will directly impact customer acquisition and lending decisions
Perform rigorous model calibration to ensure predicted probabilities accurately reflect actual outcomes and drive reliable business decisions
Design, implement, and deploy innovative data science solutions to bring data-driven prescreen strategies to life at scale
Collaborate cross-functionally with Marketing, Risk Analytics, Product, and Engineering teams to translate analytical insights into executable strategies
Monitor and optimize campaign performance metrics (response rates, conversion rates, approval rates, ROI) with continuous improvement
Requirements
4+ years of business experience working with and analyzing large datasets to solve marketing or risk problems, with demonstrated success in a similar role (prescreen campaigns, direct marketing analytics, or customer acquisition modeling)
Advanced formal training in statistics — MS or PhD in a quantitative field (Statistics, Physics, Mathematics, Economics, Engineering, Natural Sciences, Operations Research) with rigorous statistical foundations
Proven experience with model calibration techniques
Expert knowledge of Python/R and SQL for data manipulation, statistical analysis, and machine learning
Experience with AWS technical stack and data infrastructure (Spark, Hive, Hadoop, EMR, or similar distributed computing frameworks)
Deep knowledge of statistics, machine learning (logistic regression, ensemble methods, clustering), and optimization techniques
Ability to communicate complex quantitative analyses in a clear, precise, and actionable manner to both technical and non-technical stakeholders.
Applicant Tracking System Keywords
Tip: use these terms in your resume and cover letter to boost ATS matches.
MS in StatisticsPhD in StatisticsMS in MathematicsPhD in MathematicsMS in EconomicsPhD in EconomicsMS in EngineeringPhD in EngineeringMS in Natural SciencesPhD in Natural Sciences