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Applied Scientist, Worldwide Grocery Stores - Data and Science

Amazon
Posted a month ago, valid for 20 days
Location

Seattle, WA 98164, US

Salary

$142,800 - $193,200 per year

Contract type

Full Time

Health Insurance
Paid Time Off
Employee Assistance
Flexible Spending Account

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Sonic Summary

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  • Amazon's Worldwide Grocery Stores (WWGS) is looking for an Applied Scientist to enhance outbound pick efficiencies within the Amazon Grocery Network.
  • The role involves developing optimization and simulation models to lower operational costs and boost warehouse productivity, requiring a PhD or a Master's degree with 4+ years of relevant experience.
  • Candidates should have at least 3 years of experience in building business application models and be proficient in programming languages like C/C++, Python, Java, or Perl.
  • The base salary for this position ranges from $142,800 to $193,200 annually, with additional sign-on payments and restricted stock units included in the compensation package.
  • Amazon promotes an inclusive culture and offers comprehensive benefits, including health insurance, 401(k) matching, and paid time off.
Amazon's Worldwide Grocery Stores (WWGS), Data & Science team is seeking an Applied Scientist to join our under the roof (UTR) Science team, focused on improving outbound pick efficiencies across the Amazon Grocery Network. In this role, you will build optimization and simulation models that directly reduce operational costs and improve associate productivity in warehouse picking operations.

This role owns the development and deployment of mathematical optimization models for pick planning, inventory placement, and warehouse layout design. You will formulate ambiguous business problems as concrete scientific models, develop and deploy production-grade solutions, and work closely with engineering partners, product owners, and business stakeholders to deliver measurable impact.

Because UTR operations are complex and inter-connected (e.g., inbound stow vs. outbound pick), this role requires a strong understanding of these relationships and the ability to make trade-offs at the system level. You will interface directly with non-technical product owners and business leaders, manage expectations, and take an active part in influencing the feature roadmap.

Key job responsibilities
- Design, develop, and deploy mathematical optimization models (e.g., Mixed Integer Programming, meta-heuristics) to improve outbound picking efficiency, including pick planning and inventory placement.
- Build simulation models to evaluate warehouse layout designs, test optimization solutions offline, and answer strategic what-if questions.
- Formulate complex, ambiguous business problems into well-defined scientific solutions with clear objectives and constraints.
- Collaborate with engineering teams to productionize models, establish data pipelines, and create scalable architectures.
- Track solution performance post-deployment, identify issues through deep dives, and iteratively improve model quality.
- Communicate technical concepts clearly to diverse stakeholders — scientists, engineers, product managers, and business leaders — through documentation, presentations, and design reviews.
- Author peer-reviewed research papers on developed models and contribute to the internal scientific community. Basic Qualifications: - PhD, or Master's degree and 4+ years of CS, CE, ML or related field experience
- 3+ years of building models for business application experience
- Knowledge of programming languages such as C/C++, Python, Java or Perl
- Knowledge of agile development and best coding practices including peer code reviews, and unit testing
- Experience deploying machine learning or optimization models into production systems. Preferred Qualifications: - Experience in program management, logistics, operations, supply chain, transportation, or a related field
- Experience with AWS Services including EC2, Lambda, S3, DynamoDB, SQS
- Experience communicating technical concepts to non-technical audiences
- Familiarity with simulation-based optimization and discrete-event simulation
- Experience with causal inference, econometrics, or machine learning (e.g., neural networks, reinforcement learning)
- Track record of publishing research at peer-reviewed conferences or journals
- Demonstrated ability to work semi-autonomously, gathering business requirements and translating them into scientific solutions

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 - 142,800.00 - 193,200.00 USD annually



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