AmazonPosted 1w ago

Applied Science Manager, AWS Startups

Applied Science Manager, AWS Startups at Amazon scores 89 out of 100 on AI centrality, which makes it a Level 4 role on this board.

US, WA, Seattlemidfull-time$184k-$249k

AI in this role

computer-vision

Trusted by more startups around the world, AWS makes the power of cloud computing accessible for all by giving founders everywhere access to the same technology that powers the world's largest companies. With nearly two decades of experience supporting hundreds of thousands of startups, including 80% of unicorns, we democratize cloud computing to help founders bring their innovative ideas to life.

We support founders at every stage of their journey, from initial onboarding and credit programs to AI-powered guidance and scale solutions. Data is central to how we do this: it helps us identify high-potential startups early, personalize the guidance we deliver, and prioritize where we can create the most value for founders and for AWS.

We are seeking an Applied Science Manager to lead a team of applied scientists and analysts building the data and machine learning capabilities behind AWS Startups. You will own the science roadmap end-to-end, from the data foundation that unifies signals about founders, startups, and their products, through a portfolio of machine learning models, to the surfaces that put insights in the hands of the teams and products that serve startups. You will balance hands-on technical leadership with people management, setting the technical bar for your team while developing their careers.

Key job responsibilities
· Lead, coach, and grow a team of applied scientists, business intelligence engineers, and business analysts; hire and develop talent and set a high technical bar.

· Own and prioritize the team's science roadmap and set technical direction for its machine learning models and data assets, balancing rapid experimentation with production quality, cost, and reliability.

· Scope scientific projects, design and evaluate experiments, and productionize models that deliver measurable impact, establishing measurement, evaluation, and operational-excellence standards so quality and impact are quantified and defensible.

· Drive the science behind recommendation systems, startup segmentation and targeting, and fraud detection, delivering models that surface relevant opportunities, group and prioritize startups by need and fit, and protect the business from fraud and abuse.

· Partner with product, engineering, design, and go-to-market teams to translate science into scalable products, and communicate strategy, results, and trade-offs clearly to technical and non-technical leaders.

· Foster a culture of scientific rigor and rapid experimentation, and proactively identify and escalate risks with clear mitigation plans.

About the team
The AWS Startups team builds innovative products and platforms that support startup customers throughout their journey, from initial onboarding and credit programs to AI-powered guidance and scale solutions. Our portfolio serves hundreds of thousands of startup customers globally, and we partner with business development, field marketing, and solutions architecture teams worldwide. We are building the next generation of AI-native products that make world-class cloud expertise accessible to every founder.

Basic qualifications

- 2+ years of scientists or machine learning engineers management experience
- Master's degree in engineering, technology, computer science, machine learning, robotics, operations research, statistics, mathematics or equivalent quantitative field
- Master's degree, or PhD and 4+ years of building machine learning models or developing algorithms for business application experience
- Experience with any programming language such as Python, Java, C++
- Knowledge of machine learning approaches and algorithms

Preferred qualifications

- Experience building machine learning models or developing algorithms for business application
- Experience building complex software systems, especially involving deep learning, machine learning and computer vision, that have been successfully delivered to customers
- Experience with training and deploying machine learning systems to solve large-scale personalization, recommendation, or ranking 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 - 183,800.00 - 248,700.00 USD annually

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A free preview built only from this posting: what it asks for, what you could be asked in an interview, and how to adjust your resume.

Skills and AI tools this role asks for

Computer Vision

Questions you could be asked

Walk me through a computer vision problem you solved, from raw data to a deployed model.
How would you decide a model or AI system is ready to ship?
Tell me about a time a model underperformed in production. How did you find out, and what did you change?

Adapt your resume

List these exact terms on your resume: Computer Vision. An applicant tracking system matches the wording, not the idea.
Attach one line of real, concrete experience to at least one of them — a tool named with nothing behind it rarely survives a human read.
Lead with what you built, trained or shipped — this role is judged on the AI system itself, not the tools around it.

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