Staff Applied ML Scientist: Fraud & Identity (Equity)

SentiLink; Austin (TX); Híbrido; USD 200 000 - 240 000; Tempo integral

Vantagens oferecidas por esta oferta de emprego

Employer paid group health insurance

401(k) with employer match

Flexible paid time off

Regular company-wide in-person events

Home office stipend

Resumo da oferta

A leading identity solutions provider is seeking a Staff Applied ML Scientist in Austin, Texas. In this role, you will develop and maintain fraud detection models while collaborating cross-functionally to create innovative identity verification solutions. Candidates with extensive experience in data science, machine learning, and a relevant advanced degree are strongly encouraged to apply. The position offers a competitive salary range from $200,000 to $240,000 per year, with equity and benefits included.

Qualificações

• 6+ years relevant work experience & relevant PhD or 8+ years & relevant Masters.
• Proven track record of solving complex business problems with DS/ML solutions.
• Very strong in end-to-end DS development processes.

Responsabilidades

• Develop and maintain fraud detection models throughout their lifespan.
• Research and develop new SentiLink products around identity verification.
• Write production-ready code for real-time decision making.

Conhecimentos

Data Science

Machine Learning

Python

Statistical Analysis

Problem Solving

Deep Learning

Formação académica

Relevant PhD or Master's degree

Ferramentas

PostgreSQL

AWS

Descrição da oferta de emprego

A leading identity solutions provider is seeking a Staff Applied ML Scientist in Austin, Texas. In this role, you will develop and maintain fraud detection models while collaborating cross-functionally to create innovative identity verification solutions. Candidates with extensive experience in data science, machine learning, and a relevant advanced degree are strongly encouraged to apply. The position offers a competitive salary range from $200,000 to $240,000 per year, with equity and benefits included.