Staff Applied ML Scientist

About the role

As a Staff Applied ML Scientist at SentiLink, you will build core fraud detection models and advance our suite of financial risk products. You will own high‑visibility projects that require deep domain understanding, critical thinking and strong technical abilities.

Key responsibilities

• Develop and maintain fraud detection models throughout the full lifecycle, from data acquisition and featurization to productionalization and monitoring.
• Build foundational models that power SentiLink’s expanding fraud and financial‑risk offerings.
• Research new fraud types and create identity‑verification products.
• Iterate on solutions by integrating new data sources and engineering inventive features.
• Write production‑ready code for real‑time decision making.
• Design, perform and present analyses to guide data acquisition, product development, risk operations, marketing and sales.
• Collaborate with engineering, risk operations and data teams to ensure data quality and access.

Required profile

• 6+ years of relevant experience with a PhD (or 8+ years with a Master’s).
• Proven track record solving complex business problems with data‑science or ML solutions.
• Experience communicating results to senior stakeholders.
• Strong end‑to‑end DS development skills, from planning to delivery.
• Practical ML and statistical knowledge; familiarity with SOTA methods is a plus.
• Willingness to develop deep domain expertise in fraud and identity verification.
• Experience writing production code and tests.
• Detail‑oriented, able to make business‑critical decisions.
• Startup experience and ability to thrive in a fast‑paced, open‑ended environment.

Required skills

• Python (3)
• PostgreSQL
• AWS (EC2, S3, RDS, Redshift)
• Machine Learning
• Statistical analysis

What we offer

• Employer‑paid group health insurance for you and dependents.
• 401(k) plan with employer match.
• Flexible paid time off.
• Regular company‑wide in‑person events.
• Home office stipend and additional benefits.