Remote Quantitative ML Researcher (PhD New Grad)

SentiLink Corp; Austin, San Francisco, New York (TX, CA, NY); Hybrid; USD 120,000 - 220,000; Full time

Benefits offered by this job

Health insurance for you and dependets

401(k) match

Flexible PTO

Regular company-wide events

Home office stipend

Job summary

SentiLink is a leading provider of identity and risk solutions, building ML-based fraud detection models for financial services in the United States. This role emphasizes end-to-end ownership, deploying models in production, and collaborating across data, risk, and engineering teams.

Ideal candidates have advanced quantitative degrees and practical ML experience, with a strong preference for those who can work remotely within the U.S. and/or from Austin, SF, or NY offices.

Qualifications

• Advanced degree in a quantitative field or equivalent research experience.
• Strong foundation in ML, statistics, or applied data science.
• Proficient in Python and data science tools.
• Ability to translate complex problems into data-driven solutions.
• Excellent communication skills.
• Authorized to work in the United States.
• Interest in fraud, identity, and financial risk.

Responsibilities

• Develop and maintain fraud detection models across the full ML lifecycle.
• Build modeling to drive Fraud and Financial Risk products.
• Research new fraud types and identity verification.
• Design analyses to inform data acquisition, product development, risk operations priorities, marketing, and sales efforts.
• Write production-ready code for real-time decisions.
• Collaborate with engineering, risk operations, and data acquisitions teams.
• Operate with end-to-end ownership in a fast-paced environment.

Skills

Machine learning

Statistics

Python

Data analysis

Communication

Education

Bachelor's/Master's/PhD in a quantitative field

Tools

PostgreSQL

AWS

Job description

SentiLink is a leading provider of identity and risk solutions, building ML-based fraud detection models for financial services in the United States. This role emphasizes end-to-end ownership, deploying models in production, and collaborating across data, risk, and engineering teams.

Ideal candidates have advanced quantitative degrees and practical ML experience, with a strong preference for those who can work remotely within the U.S. and/or from Austin, SF, or NY offices.