Data Scientist

ABOUT HAPPY MONEY

Happy Money is a consumer finance company that empowers people to achieve their goals through simple, transparent loans funded by trusted financial institutions. Through its proprietary lending platform, Hive, Happy Money delivers a fully digital experience that simplifies borrowing for consumers and helps credit unions, banks and asset managers diversify balance sheets and scale with confidence. Happy Money’s end-to-end loan origination and participation programs provide partners with turnkey access to high-performing assets, underpinned by disciplined credit expertise and robust risk management. Together with its lending partners, Happy Money has originated more than $6.5 billion in loans to help nearly 350,000 people take greater control of their financial futures. Learn more at happymoney.com. 

Role: 6 month Full-Time Contract (1099)Pay: $75 - $80/hourWork Location: Fully Remote (Must reside in AL, AZ, DE, FL, GA, IL, IA, KS, MI, MN, NV, NC, OH, PA, SC, TN, TX, UT, VA, or WI)Visa Sponsorship: Not available

This contract is based on a 6 month, ∼40-hour per week contract with the potential to convert to a full-time, salaried employee status (with full benefits) based on performance and business needs.Reporting to the Director of Data Science, in this role you'll build machine learning models that directly drive customer acquisition and lending decisions. Your models will drive our marketing strategies, determine which consumers receive our credit offers, and fuel Happy Money's rapid growth. You'll work across the full modeling lifecycle in a high-impact role where statistical rigor translates directly into business results. Join us at an exciting inflection point where your contributions will shape how Happy Money scales responsibly.

• 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

• 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