{"id":894597,"url":"https://alion.io/job/plooto-decision-scientist-risk","title":"Decision Scientist, Risk","company":{"id":58155,"name":"Plooto","domain":"plooto.com","url":"https://alion.io/company/plooto","size_band":"51-200","is_staffing_agency":false,"employer_type":"direct","is_intermediary":false,"listed_via":null,"ats_vendor":"Greenhouse","truth_index":null},"role":"Finance","role_family":"Finance","seniority":"junior","employment_type":null,"work_mode":"remote","remote_scope":"stated_countries","remote_scope_basis":"board_field","remote_working_hours":null,"hiring_geo_confidence":"structured","locations":["Toronto, Canada","Canada"],"countries":["CA"],"hiring_countries":["CA"],"hiring_countries_total":1,"salary":null,"salary_estimate":{"min_usd":39000,"max_usd":66000,"period":"year","method":"role_seniority_country_cell","sample_n":36},"experience_years_min":2,"visa_sponsorship":false,"relocation_package":false,"has_equity":false,"technologies":[{"name":"Machine Learning","optional":false},{"name":"Python","optional":false},{"name":"SQL","optional":false}],"status":"live","first_seen_at":"2026-08-20T03:21:21Z","employer_posted_date":"2026-08-20","last_verified_at":"2026-10-25T09:02:27Z","board_verified":true,"closed_at":null,"days_open":66,"trust":{"level":"ok","repost_count":null,"flags":[],"days_open":66},"description":"Role Overview\nThe 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.\nThis 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.\nWhat You’ll Do\nMonitor and analyze the performance of payment risk rules, thresholds, and manual-review strategies.\nAnalyze transaction and customer data to identify drivers of false positives and unnecessary manual reviews.\nDevelop and evaluate customer and transaction risk segments to improve decision precision.\nIdentify and recommend changes to risk rules and thresholds within established risk and loss parameters.\nDesign and evaluate experiments to assess new risk features, rules, and decision strategies.\nInvestigate fraud and payment-risk patterns and translate findings into practical recommendations.\nMeasure the impact of risk changes across review rates, losses, customer friction, payment delays, and operational workload.\nBuild and maintain recurring monitoring and reporting to identify changes in risk performance and emerging issues.\nPartner with Risk Operations to understand manual-review drivers and identify opportunities for automation and process improvement.\nUse AI and automation tools to accelerate analysis, monitoring, and experimentation.\nWhat Success Looks Like\nAnalysis identifies actionable opportunities to improve payment-risk decisioning.\nManual payment review rates decline as risk rules, segmentation, and automation improve.\nPayment losses remain within established risk thresholds as manual intervention is reduced.\nFewer legitimate payments experience unnecessary manual-review delays.\nRisk Operations capacity scales more efficiently as payment volume grows.\nRisk rules and thresholds have clear performance monitoring and measurable outcomes.\nEmerging fraud and payment-risk trends are surfaced quickly and translated into appropriate action.\nWhat We’re Looking For\n2-3 years of relevant experience in risk analytics, fraud strategy, payments risk, decision science, or a related quantitative role.\nStrong SQL skills, including experience analyzing large transactional datasets.\nStrong Python skills for analysis, experimentation, automation, and monitoring.\nExperience working with risk rules, machine learning models, segmentation, fraud signals, or decision strategies.\nStrong understanding of false positives, precision/recall, threshold optimization, experimentation, and risk-based decisioning.\nAbility to evaluate trade-offs between customer friction and financial loss within established risk parameters.\nStrong analytical judgment and comfort working with imperfect or incomplete data.\nAbility to translate complex analysis into clear, practical recommendations.\nComfortable working independently on defined problems and collaborating with cross-functional partners.\nWhy Become a Plootonian?\nYou'll help lay the foundation for how Plooto hires during our next stage of growth.\nYou'll shape recruiting strategy, influence leaders across the company, experiment with AI-native ways of working, and build hiring practices that will have a lasting impact long after your engagement is complete.\nIf you're energized by building, coaching, finding remarkable people, and leaving things better than you found them, we'd love to meet you.\nOur Commitment to Inclusion\nPlooto is committed to building an inclusive workplace where people of all backgrounds, experiences, and perspectives can thrive. We know that great teams are built through diverse thinking, and we encourage you to apply even if your experience doesn't align perfectly with every qualification listed.\nAccommodation is available throughout the recruitment process in accordance with applicable accessibility legislation. If you require accommodation at any stage, please let us know-we're happy to work with you.\nThis posting is for one available position. 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