Security Data Science Manager

VulnU; Seattle, Northern (WA, KY); Híbrido; USD 175 000 - 237 000; Tempo integral

Vantagens oferecidas por esta oferta de emprego

Paid parental leave

Retirement match

Equity

Flexible PTO

Resumo da oferta

Amazon is seeking a Data Science Manager for Security Compliance Engineering in Seattle. You will lead a combined data science and data engineering team tasked with turning security guidance into measurable, scalable outcomes across a vast product portfolio.

As a senior leader, you will own metrics, guide model-driven risk identification, and align science with business leadership to reduce production risk and bias in ML systems.

Qualificações

• 5+ years building quantitative solutions as a scientist or science manager.
• 2+ years of scientists or machine learning engineers management experience.
• 5+ years applying statistical models for large-scale applications and automated analytical systems.
• PhD in computer science, mathematics, statistics, machine learning or equivalent quantitative field.
• Knowledge of Python or R or other scripting language.
• Experience in at least one ML area (NLP, Regression, Classification, Clustering, Anomaly Detection).
• Experience with fairness in ML/AI to detect and remove bias.

Responsabilidades

• Set the strategy, roadmap and technical direction for a combined data science and data engineering team.
• Own metrics to measure embedded security effectiveness and ensure guidance improves production outcomes.
• Lead data science to identify risk across a large portfolio and define controls each service needs.

Conhecimentos

Quantitative solutions

Science management

Statistical modeling

Python/R

ML experience

Fairness in ML

Formação académica

PhD in quantitative field

Ferramentas

Python

R

Descrição da oferta de emprego

Amazon is seeking a Data Science Manager for Security Compliance Engineering in Seattle. You will lead a combined data science and data engineering team tasked with turning security guidance into measurable, scalable outcomes across a vast product portfolio.

As a senior leader, you will own metrics, guide model-driven risk identification, and align science with business leadership to reduce production risk and bias in ML systems.