AmazonPosted 1mo ago

Sr. Applied Scientist, PXT Central Science

Sr. Applied Scientist, PXT Central Science at Amazon scores 90 out of 100 on AI centrality, which makes it a Level 4 role on this board.

US, CA, San Franciscoseniorfull-time$192k-$260k

AI in this role

tensorflowscikit-learn

nlp

We are seeking a Senior Applied Scientist to build production machine learning systems that solve complex business problems at scale. You will design, develop, and deploy ML solutions that directly impact millions of users and drive strategic decision-making across the organization.

In this role, you will work on challenging problems spanning predictive modeling, natural language processing, recommendation systems, and generative AI applications. You will collaborate with cross-functional teams to translate ambiguous business challenges into rigorous technical solutions.

Key job responsibilities
- Design and deploy large-scale machine learning systems in production environments
- Develop innovative ML solutions using state-of-the-art techniques including deep learning, NLP, and generative AI
- Create ML solutions that personalize manager onboarding and development experiences — identifying individual capability gaps, recommending tailored learning pathways, and measuring development effectiveness across diverse manager populations and contexts
- Partner to build causal inference models and experimental frameworks to measure impact
- Collaborate with product managers, engineers, and business leaders to define technical roadmaps
- Communicate complex technical concepts to diverse audiences, from technical peers to senior leadership

About the team
The People eXperience and Technology Central Science Team (PXTCS) uses economics, behavioral science, statistics, and machine learning to proactively identify mechanisms and process improvements which simultaneously improve Amazon and the lives, wellbeing, and the value of work to Amazonians. We are an interdisciplinary team that combines the talents of science and engineering to develop and deliver solutions that measurably achieve this goal.

Basic qualifications

- 3+ years of building machine learning models for business application experience
- PhD, or Master's degree and 6+ years of applied research experience
- Experience programming in Java, C++, Python or related language
- Experience with neural deep learning methods and machine learning

Preferred qualifications

- Experience with modeling tools such as R, scikit-learn, Spark MLLib, MxNet, Tensorflow, numpy, scipy etc.
- Experience with large scale distributed systems such as Hadoop, Spark etc.
- Experience in driving end to end delivery, and communicating results to senior leadership
- Experience with leading experienced scientists as well as having a record of developing junior members from academia or industry to a career track in a business environment
- Experience with building LLMs, generative AI applications on AWS
- Experience with personalization, recommendation systems, or adaptive learning systems

Amazon is an equal opportunity employer and does not discriminate on the basis of protected veteran status, disability, or other legally protected status.

Los Angeles County applicants: Job duties for this position include: work safely and cooperatively with other employees, supervisors, and staff; adhere to standards of excellence despite stressful conditions; communicate effectively and respectfully with employees, supervisors, and staff to ensure exceptional customer service; and follow all federal, state, and local laws and Company policies. Criminal history may have a direct, adverse, and negative relationship with some of the material job duties of this position. These include the duties and responsibilities listed above, as well as the abilities to adhere to company policies, exercise sound judgment, effectively manage stress and work safely and respectfully with others, exhibit trustworthiness and professionalism, and safeguard business operations and the Company’s reputation. Pursuant to the Los Angeles County Fair Chance Ordinance, we will consider for employment qualified applicants with arrest and conviction records.

Pursuant to the San Francisco Fair Chance Ordinance, we will consider for employment qualified applicants with arrest and conviction records.

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, CA, San Francisco - 192,200.00 - 260,000.00 USD annually
USA, MA, Boston - 167,100.00 - 226,100.00 USD annually
USA, VA, Arlington - 167,100.00 - 226,100.00 USD annually
USA, WA, Bellevue - 167,100.00 - 226,100.00 USD annually
USA, WA, Seattle - 167,100.00 - 226,100.00 USD annually

Prepare for this job

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

NlpTensorFlowscikit-learn

Questions you could be asked

What NLP problem have you worked on, and how did you measure whether it actually worked?
Walk me through how you've used TensorFlow in your day-to-day work.
What are the limits of scikit-learn that you've run into, and how did you work around them?
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: Nlp, TensorFlow, and scikit-learn. 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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