Lead - POC Data Science

## Who we are: We are a leader in fraud prevention and AML compliance. Our platform uses device intelligence, behavior biometrics, machine learning, and AI to stop fraud before it happens. Today, over 300 banks, retailers, and fintechs worldwide use Sardine to stop identity fraud, payment fraud, account takeovers, and social engineering scams. We have raised $145M from world-class investors, including Andreessen Horowitz, Activant, Visa, Experian, FIS, and Google Ventures. ## Our culture: - We have hubs in the Bay Area, NYC, Austin, Toronto, and São Paulo. However, we maintain a remote-first work culture. #WorkFromAnywhere - We hire talented, self-motivated individuals with extreme ownership and high growth orientation. - We value performance and not hours worked. We believe you shouldn't have to miss your family dinner, your kid's school play, friends get-together, or doctor's appointments for the sake of adhering to an arbitrary work schedule.

## What you'll be doing: - Lead and develop a team of IC data scientists — set direction, unblock work, run 1:1s, and grow each person's scope and impact - Own POC/POV delivery — partner directly with enterprise customers to demonstrate fraud-loss reduction and platform ROI, from first data pull through to stakeholder readout - Stay hands-on in the technical work — build or review ML models, conduct in-depth fraud analyses, and ship production-grade solutions alongside your team - Define and track performance metrics — design dashboards and reporting frameworks to measure the effectiveness of risk strategies across clients - Translate client problems into data solutions — act as a senior point of contact for fraud challenges, turning complex findings into clear recommendations - Partner cross-functionally with Engineering, Product, and GTM to scope work, influence the roadmap, and ensure fraud solutions and models get instrumented and scaled correctly - Drive experimentation — support A/B testing to safely validate new strategies before full rollout - Raise the bar on craft — mentor IC data scientists on modeling rigor, storytelling with data, and client communication

## What you'll need: - 7–10 years of experience in fraud/risk data science and analytics with demonstrated impact in fraud, payments, or fintech - 2–3 years in a people leadership role (team lead, manager, or tech lead with direct reports) — you've coached data scientists and helped them grow - Strong hands-on technical skills — Python and SQL are essential; Spark, Kafka, or feature stores are a plus - Experience delivering POC/POV engagements with measurable customer outcomes - Proven track record with applied ML in fraud or risk — anomaly detection, classification, and graph analytics in production - Expertise in BI and dashboarding — Sigma, Tableau, Metabase, or equivalent - Strong communication and stakeholder management — able to translate complex model outputs for both technical and non-technical audiences, including clients and execs - Bias toward action and ownership — you don't wait to be unblocked

## Benefits we offer: - Generous compensation in cash and equity - Early exercise for all options, including pre-vested - Work from anywhere: Remote-first Culture - Flexible paid time off and Year-end break - Health insurance, dental, and vision coverage for employees and dependents - *US and Canada specific* - 4% matching in 401k / RRSP - *US and Canada specific* - MacBook Pro delivered to your door - One-time stipend to set up a home office — desk, chair, screen, etc. - Monthly meal stipend - Monthly social meet-up stipend - Annual health and wellness stipend - Annual Learning stipend

Sardine is an AI-powered risk platform specializing in fraud prevention, compliance, and credit underwriting for financial services, including banks, fintechs, neobanks, and crypto companies. It uses behavioral analysis, device intelligence, behavioral biometrics, KYC/AML tools, transaction monitoring, and case management to detect and stop financial crimes like identity theft, payment fraud, and money laundering.