AmazonSr. Applied Scientist, Amazon Private Brands, Amazon Private Brands Selection Guidance
Amazon is hiring a Sr. Applied Scientist, Amazon Private Brands, Amazon Private Brands Selection Guidance in Vancouver, Canada. It pays CAD 196k-CAD 327k a year and Level rates it ; you can apply on Amazon.
Level's read on this job
- Level rates this applied scientist role Builds AI ●●●●: the job is building AI systems.
- Tools and skills the posting cites include TensorFlow and scikit-learn.
- Level classifies it as a senior role.
- Level lists 20,000+ other open roles at Amazon.
- The role was posted in the last few days.
AI in this role
The Selection Guidance team applies Generative AI, Machine Learning, Statistics, and Economics solutions to drive our private brands product assortment, strategic business decisions, and product inputs such as title, price, merchandising and ordering. We are an interdisciplinary team of Scientists, Economists, Engineers, and Product Managers incubating and building day one solutions using novel technology, to solve some of the toughest business problems at Amazon.
As a Sr. Applied Scientist you apply state-of-the-art research to business problems, invent novel solutions and prototypes, and directly contribute to bringing your ideas to life through production implementation. Current research areas include named entity recognition, product substitutes, pricing optimization, agentic AI, and large language models. You will review and guide scientists across the team on their designs and implementations, and raise the team bar for science research and development.
This is a unique, high visibility opportunity for someone who wants to develop ambitious science solutions and have direct business and customer impact.
Key job responsibilities
- Partner with business stakeholders to deeply understand APB business problems and frame ambiguous business problems as science problems and solutions.
- Adapt and apply state-of-the-art machine learning solutions to business problems.
Invent novel science solutions, develop prototypes, and deploy production software to solve business problems.
- Review and guide science solutions across the team.
- Publish and socialize your and the team's research across Amazon and external avenues as appropriate
- Leverage industry best practices to establish repeatable applied science practices, principles & processes.
A day in the life
You might start your morning reviewing experiment results from a new model architecture you proposed, then meet with engineering partners to discuss how to integrate your latest prototype into a production pipeline. After lunch, you lead a design review for a colleague's approach to a retrieval problem, offering alternative scientific directions grounded in your deep domain expertise. Later, you spend focused time writing code for a novel training methodology, and close the day drafting a research proposal that could open an entirely new capability for your team.
Basic qualifications
- PhD, or Master's degree and 6+ years of applied research experience
- Experience programming in Java, C++, Python or related language
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.
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. As a total compensation company, Amazon's package may include other elements such as sign-on payments and restricted stock units (RSUs). Final compensation will be determined based on factors including experience, qualifications, and location. Amazon offers comprehensive benefits including health insurance (medical, dental, vision, prescription, basic life & AD&D insurance), Registered Retirement Savings Plan (RRSP), Deferred Profit Sharing Plan (DPSP), paid time off, and other resources to improve health and well-being. We thank all applicants for their interest, however only those interviewed will be advised as to hiring status.
CAN, BC, Vancouver - 195,900.00 - 327,200.00 CAD annually
How we rate this
Sr. Applied Scientist, Amazon Private Brands, Amazon Private Brands Selection Guidance at Amazon rates 86 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
- Typical applied scientist job
- $172k
Use the typical applied scientist figure as a reference when pay comes up. See applied scientist jobs
Questions you could be asked
- What's a project where you used TensorFlow hands-on?
- Walk me through how you've used scikit-learn in your day-to-day work.
- 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: 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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