Finding where risk really lives, using data and models to surface the issues and high-risk code that matter most before they reach production.
Answering hard measurement questions at scale: is our guidance accurate, are builders acting on it, are fewer issues reaching production?
Working across disciplines, using science, analytics, and engineering together so each makes the others stronger.
Standing up something new: shaping the strategy, culture, and standards for a team ready for its first dedicated data leader.
Growing engineers and scientists and watching them take on bigger scope because of how you invested in them.
Key job responsibilities
Set the strategy, roadmap, and technical direction for a combined data science and data engineering team, turning an ambiguous, fast-moving security mission into clear, prioritized work and partnering with tech and business leadership to shape the vision.
Own how we measure whether embedded security is working: build the metrics, experiments, and analysis that show whether guidance is accurate, whether builders act on it, and whether fewer issues reach production because of it.
Lead the data science behind risk identification and the compounding feedback loop: surface where real risk lives across a large portfolio, scope which controls each service needs, and make sure every issue found feeds back into smarter guidance so the same problem does not recur.
Determine the right mix of science, data engineering, and BI resources, use those disciplines to complement rather than duplicate one another, and lead senior individual contributors credibly across both the science and engineering sides of the team.
Hold the bar on quality and delivery: judge whether outputs are trustworthy and ready to inform decisions or ship, without letting work be under-scoped or over-engineered, and invest in the data foundation of trusted data, reliable pipelines, and reproducible research. Own the results and credit the people who deliver them.
Grow the team and manage ambiguity: develop scientists and engineers into bigger scope, support in-flight discipline transitions, establish the shared standards and data culture that let the team deliver without you, and mitigate risks and escalate with good judgment before they become roadblocks.
About the team
Diverse Experiences
Amazon Security values diverse experiences. Even if you do not meet all of the qualifications and skills listed in the job description, we encourage candidates to apply. If your career is just starting, hasn’t followed a traditional path, or includes alternative experiences, don’t let it stop you from applying.
Why Amazon Security?
At Amazon, security is central to maintaining customer trust and delivering delightful customer experiences. Our organization is responsible for creating and maintaining a high bar for security across all of Amazon’s products and services. We offer talented security professionals the chance to accelerate their careers with opportunities to build experience in a wide variety of areas including cloud, devices, retail, entertainment, healthcare, operations, and physical stores.
Inclusive Team Culture
In Amazon Security, it’s in our nature to learn and be curious. Ongoing DEI events and learning experiences inspire us to continue learning and to embrace our uniqueness. Addressing the toughest security challenges requires that we seek out and celebrate a diversity of ideas, perspectives, and voices.
Training & Career Growth
We’re continuously raising our performance bar as we strive to become Earth’s Best Employer. That’s why you’ll find endless knowledge-sharing, training, and other career-advancing resources here to help you develop into a better-rounded professional.
Work/Life Balance
We value work-life harmony. Achieving success at work should never come at the expense of sacrifices at home, which is why flexible work hours and arrangements are part of our culture. When we feel supported in the workplace and at home, there’s nothing we can’t achieve.
5+ years of building quantitative solutions as a scientist or science manager experience
2+ years of scientists or machine learning engineers management experience
5+ years of applying statistical models for large-scale application and building automated analytical systems experience
PhD in computer science, mathematics, statistics, machine learning or equivalent quantitative field
Knowledge of Python or R or other scripting language
Experience in a least one area of Machine Learning (NLP, Regression, Classification, Clustering, or Anomaly Detection)
Experience with fairness in machine learning and artificial intelligence to detect and remove bias in ML/AI systems
Amazon is an equal opportunity employer and does not discriminate on the basis of protected veteran status, disability, or other legally protected status.
Our inclusive culture empowers Amazonians to deliver the best results for our customers. If you have a disability and need a workplace accommodation or adjustment during the application and hiring process, including support for the interview or onboarding process, please visit https://amazon.jobs/content/en/how-we-hire/accommodations for more information. If the country/region you’re applying in isn’t listed, please contact your Recruiting Partner.
The base salary range for this position is listed below. Your Amazon package will include sign-on payments and restricted stock units (RSUs). Final compensation will be determined based on factors including experience, qualifications, and location. Amazon also offers comprehensive benefits including health insurance (medical, dental, vision, prescription, Basic Life & AD&D insurance and option for Supplemental life plans, EAP, Mental Health Support, Medical Advice Line, Flexible Spending Accounts, Adoption and Surrogacy Reimbursement coverage), 401(k) matching, paid time off, and parental leave. Learn more about our benefits at https://amazon.jobs/en/benefits.
USA, WA, Seattle - 175,100.00 - 236,900.00 USD annually
