AmazonSenior Applied Scientist, Selling Partner Selection Success
Amazon is hiring a Senior Applied Scientist, Selling Partner Selection Success in Seattle, United States. It pays $167k-$226k a year and Level rates it ; you can apply on Amazon.
Level's read on this job
- On Level's scale, this applied scientist role is Builds AI ●●●●: the job is building AI systems.
- Pay on this posting starts in line with the typical applied scientist posting on Level.
- The posting mentions TensorFlow, scikit-learn and MLOps among its tools and skills.
- Amazon has 22,000+ other open roles on Level.
- Posted in the last few days.
AI in this role
We are looking for a Senior Applied Scientist to lead us to identify data-driven insight and opportunities to improve our SP recruitment strategy and drive new seller success. As a successful applied scientist on our talented team of scientists and engineers, you will solve complex problems to identify actionable opportunities, and collaborate with engineering, research, and business teams for future innovation. You are sophisticated user and builder that can leverage the latest scientific methods, including statistics, machine learning, deep learning and GenAI technology and are able to put them in production to answer specific business questions, solve complex problems, and collaborate with engineering, research, and business teams to deliver optimized experience (AI infused products, agentic experiences) on behalf of our sellers. You are an expert at synthesizing and communicating insights and recommendations to audiences of varying levels of technical sophistication. You will continue to contribute to the research community, by working with scientists across Amazon, as well as collaborating with academic researchers and publishing papers (www.aboutamazon.com/research).
Key job responsibilities
As a Sr. Applied Scientist in the team, you will:
- Identify opportunities to improve SP growth and translate those opportunities into science problems via principled statistical solutions (e.g. ML, RL, GenAI and Agentics).
- Mentor and guide the applied scientists in our organization and hold us to a high standard of technical rigor and excellence in MLOps.
- Design and lead roadmaps for complex science projects to help SP have a delightful selling experience while creating long term value for our shoppers.
- Work with our engineering partners and draw upon your experience to meet latency and other system constraints.
- Identify untapped, high-risk technical and scientific directions, and simulate new research directions that you will drive to completion and deliver.
- Be responsible for communicating our science innovations to the broader internal & external scientific community.
Basic qualifications
- 4+ years of applied research experience
- 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.
- Demonstrated experience leveraging generative AI tools to enhance workflow efficiency and productivity, with the ability to craft effective prompts and critically evaluate AI-generated outputs in a professional setting
- Experience identifying opportunities to integrate AI solutions into products and services to drive business value
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 - 167,100.00 - 226,100.00 USD annually
How we rate this
Senior Applied Scientist, Selling Partner Selection Success at Amazon rates 89 out of 100 for how much of the daily work is AI. That makes it Builds AI (AI Level 4 of 4). The level is about AI in the job, not seniority.
Builds AI. The job is building AI systems.
- ●●●● Builds AI80 to 100
- ●●●○ Works on AI60 to 79
- ●●○○ Uses AI40 to 59
- ●○○○ Little AI0 to 39
Levels come from how often the tools, models and workflows of the role are named in the posting itself. Read the description and count the AI tools, models and workflows it names.
Prepare for this job
Built from this posting and from similar jobs on Level.
Skills and AI tools this role asks for
Also common in applied scientist jobs at this level
Pay
- This job starts at
- $167k
- Typical applied scientist job
- $172k
This posting starts close to the typical applied scientist job. See applied scientist jobs
Questions you could be asked
- How do you monitor a model once it's live, and how do you know it needs retraining?
- 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: ML Ops, 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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