Role OverviewThe Risk Decision Scientist will support the ongoing optimization of Plooto's payment risk decisioning, using data, experimentation, and decision-science techniques to help reduce unnecessary manual reviews while maintaining strong loss performance.
This role will analyze the performance of risk rules, thresholds, customer segments, and review strategies and identify opportunities to improve decision precision. Working closely with Risk Operations, Product, Engineering, and Data, the successful candidate will turn analysis into practical recommendations that reduce customer friction and help Plooto's risk controls scale as payment volume grows.
What You'll Do- Monitor and analyze the performance of payment risk rules, thresholds, and manual-review strategies.
- Analyze transaction and customer data to identify drivers of false positives and unnecessary manual reviews.
- Develop and evaluate customer and transaction risk segments to improve decision precision.
- Identify and recommend changes to risk rules and thresholds within established risk and loss parameters.
- Design and evaluate experiments to assess new risk features, rules, and decision strategies.
- Investigate fraud and payment-risk patterns and translate findings into practical recommendations.
- Measure the impact of risk changes across review rates, losses, customer friction, payment delays, and operational workload.
- Build and maintain recurring monitoring and reporting to identify changes in risk performance and emerging issues.
- Partner with Risk Operations to understand manual-review drivers and identify opportunities for automation and process improvement.
- Use AI and automation tools to accelerate analysis, monitoring, and experimentation.
What Success Looks Like- Analysis identifies actionable opportunities to improve payment-risk decisioning.
- Manual payment review rates decline as risk rules, segmentation, and automation improve.
- Payment losses remain within established risk thresholds as manual intervention is reduced.
- Fewer legitimate payments experience unnecessary manual-review delays.
- Risk Operations capacity scales more efficiently as payment volume grows.
- Risk rules and thresholds have clear performance monitoring and measurable outcomes.
- Emerging fraud and payment-risk trends are surfaced quickly and translated into appropriate action.
What We're Looking For- 2-3 years of relevant experience in risk analytics, fraud strategy, payments risk, decision science, or a related quantitative role.
- Strong SQL skills, including experience analyzing large transactional datasets.
- Strong Python skills for analysis, experimentation, automation, and monitoring.
- Experience working with risk rules, machine learning models, segmentation, fraud signals, or decision strategies.
- Strong understanding of false positives, precision/recall, threshold optimization, experimentation, and risk-based decisioning.
- Ability to evaluate trade-offs between customer friction and financial loss within established risk parameters.
- Strong analytical judgment and comfort working with imperfect or incomplete data.
- Ability to translate complex analysis into clear, practical recommendations.
- Comfortable working independently on defined problems and collaborating with cross-functional partners.
This posting is for one available position. Compensation will be determined based on the successful candidate's knowledge, skills, experience, and overall alignment with the role.
