Quant Researcher (PhD) – Real‑World Fraud ML (Remote)

SentiLink; Austin (TX); Remote; USD 120,000 - 220,000; Full time

Benefits offered by this job

Employer paid group health insurance

401(k) plan with employer match

Flexible paid time off

Regular company-wide in‑person events

Home office stipend

Job summary

SentiLink seeks a Quantitative Researcher to build core products: models that detect fraud and advance risk analytics. This role suits new PhD graduates or early-career researchers applying ML to real‑world fraud.

You will develop and ship ML models in production, gain exposure across the full lifecycle, and work with teams across the company on data, features, and deployment, with remote-friendly US options.

Qualifications

• Degree in statistics, CS, physics, math or related field or equivalent experience
• Strong ML / statistics foundation
• Experience with Python and data science tools
• Ability to analyze complex problems and deliver data-driven solutions
• Nice to have fraud, identity, risk domain interest
• Eligible to work in the United States

Responsibilities

• Develop and maintain fraud detection models across full lifecycle.
• Build modeling for Fraud and Financial Risk products.
• Research new fraud types and identity verification products.
• Perform feature engineering and data source integration.
• Write production-ready code for real-time decisions.
• Design analyses to inform data acquisition, product development, risk operations.
• Collaborate with engineering, risk operations, and data teams to access data

Skills

ML fundamentals

Python

Statistics

Data analysis

Communication

Fraud/risk domain interest

Education

Bachelor/Master/PhD in quantitative field

Tools

PostgreSQL

AWS

EC2

S3

RDS

Redshift

Job description

SentiLink seeks a Quantitative Researcher to build core products: models that detect fraud and advance risk analytics. This role suits new PhD graduates or early-career researchers applying ML to real‑world fraud.

You will develop and ship ML models in production, gain exposure across the full lifecycle, and work with teams across the company on data, features, and deployment, with remote-friendly US options.