Applied ML Scientist — Fraud & Risk (Remote)

SentiLink; Los Angeles (CA); On-site; USD 180,000 - 220,000; Full time

Benefits offered by this job

Employer paid health insurance

401(k) plan with employer match

Flexible paid time off

Home office stipend

Regular company events

Job summary

SentiLink is seeking an Applied ML Scientist to build our core fraud-detection models and expand our risk-product suite. You will apply ML to real-world fraud data, from research to deployment, and own end-to-end model development in production environments.

You will collaborate with engineering, risk operations, and data acquisitions to access data, maintain quality, and deliver data-driven insights. Remote within the U.S. with offices in Austin, SF, NY.

Qualifications

• Strong foundation in machine learning, statistics, or applied data science.
• Experience with Python and common data science tools through coursework, research, internships, or personal projects.
• Demonstrated ability to analyze complex problems and build data-driven solutions.

Responsibilities

• Develop and maintain fraud detection models through the full model development lifecycle.
• Build foundational modeling for Fraud and Financial Risk products.
• Research new fraud types and develop new SentiLink products around identity verification.
• Write production-ready code for real-time decision making by partners.
• Design analyses to inform data acquisition, product development, and risk operations priorities.

Skills

Machine learning

Statistics

Data science

Python

Education

Bachelor’s/Master’s/PhD in quantitative field

Tools

Python

PostgreSQL

AWS

Job description

SentiLink is seeking an Applied ML Scientist to build our core fraud-detection models and expand our risk-product suite. You will apply ML to real-world fraud data, from research to deployment, and own end-to-end model development in production environments.

You will collaborate with engineering, risk operations, and data acquisitions to access data, maintain quality, and deliver data-driven insights. Remote within the U.S. with offices in Austin, SF, NY.