About the Role

As a Senior Staff Decision Scientist on the Risk team, you will own the strategy, design, and optimization of Flex's fraud and account-security decisioning stack. You'll set policy across the customer lifecycle — onboarding, identity, transaction, and account takeover — balancing fraud loss, customer experience, and growth across millions of transactions.

This is a senior individual-contributor role. You will be the policy voice in the room, setting direction for how Flex detects and prevents fraud, and working across Risk, Product, Engineering, Compliance, and Data to ship and measure that policy in production.

What You'll Do

• Own and evolve Flex's fraud-prevention and account-security policy across onboarding, identity/KYC, payments, and account-takeover surfaces
• Design, ship, and tune detection rules and decisioning logic in the risk decision engine — from signal definition to live rollout
• Use large-scale transaction, identity, and behavioral data to identify emerging fraud patterns and quantify their loss impact
• Build models and forecasts to size the fraud/loss and customer-experience tradeoffs of every policy change before it ships
• Design shadow tests and experiments, and measure the tradeoff between fraud loss, false-positive friction, and approval/growth
• Set the fraud-loss and dispute/chargeback strategy, and define what "good" looks like across the detection portfolio
• Define the metrics, monitoring, and dashboards that keep fraud performance observable and catch regressions early
• Partner closely with Product, Engineering, Compliance, and Operations to take policy from analysis to production impact
• Communicate clear, actionable recommendations — and the risk tradeoffs behind them — to leadership and stakeholders

What We're Looking For

• 8+ years in fraud, risk, decision science, or platform analytics, with deep experience owning fraud or risk policy end-to-end
• Strong hands-on skills in Python and SQL, and comfort working with large, messy transactional datasets
• Experience with fraud and risk concepts — rule/policy design, loss modeling, cohort analysis, and precision/recall tradeoffs
• Track record building or operating detection systems on high-scale transactional platforms (payments, lending, marketplaces)
• Fluency with fraud signals and typologies: identity/KYC, account takeover, first-party fraud, payment fraud, and disputes/chargebacks
• Strong product and business intuition — you care about shipping impact and reducing loss, not just analysis
• Comfortable operating cross-functionally and owning ambiguous, high-leverage problem spaces
• Background in fintech, payments, or large tech platforms is a strong plus