Applied Scientist, Fraud & Identity (Remote‑Friendly)

SentiLink; Seattle (WA); On-site; USD 120,000 - 220,000; Full time

Benefits offered by this job

Employer paid health insurance for you

401(k) with employer match

Flexible paid time off

In-person company events

Home office stipend

Job summary

SentiLink, a leader in identity and risk solutions, seeks an Applied Scientist to build fraud-detection models and expand its ML products in financial risk. You’ll work on end-to-end model development, from research to production deployment, in a fast-paced, data-driven environment.

Ideal candidates have PhD/masters or equivalent, strong Python skills, and a passion for applying ML to real-world fraud. Remote-friendly within the United States, with preferences for Austin, SF, or NY offices.

Qualifications

• Degree in a quantitative field or equivalent experience
• Experience with ML, statistics, or applied data science
• Python and data science tooling experience
• Strong communication and problem-solving skills

Responsibilities

• Develop fraud detection models through full ML lifecycle
• Build foundational modeling for Fraud and Financial Risk products
• Research new fraud types and identity verification approaches
• Iterate with data sources and engineering to improve features
• Write production-ready code for real-time decisions
• Design analyses to inform data acquisition and product direction
• Collaborate with risk ops and data acquisition teams

Skills

Machine learning

Statistics

Python

Data science

Communication

Problem solving

Code quality

Attention to detail

Education

Bachelor/Master/PhD in Statistics/CS/Physics/Mathematics

Tools

PostgreSQL

AWS

Job description

SentiLink, a leader in identity and risk solutions, seeks an Applied Scientist to build fraud-detection models and expand its ML products in financial risk. You’ll work on end-to-end model development, from research to production deployment, in a fast-paced, data-driven environment.

Ideal candidates have PhD/masters or equivalent, strong Python skills, and a passion for applying ML to real-world fraud. Remote-friendly within the United States, with preferences for Austin, SF, or NY offices.