Role at a glance
- Job function
-
AI & Data AI Research & Applied Science
- Salary
- Not Disclosed
- Location
- Bellevue, Washington, United States
- Work arrangement
- On-site
- Employment
- Full-time
- Experience
- PhD in operations research, applied mathematics, theoretical computer science, or equivalent, or Master's degree and 4+ years of machine...
- Education
- PhD in operations research, applied mathematics, theoretical computer science, or equivalent, or Master's degree and 4+ years of machine...
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Role Summary
The Applied Scientist develops scientific solutions for sourcing and vendor experience systems, working with software engineers, product managers, and business teams to define problems and address them. The work applies methods including optimization, causal inference, and machine learning/deep learning to challenges involving buying decisions, vendor behavior, supply risk, and inbound signals.
What You'll Do
- Set the team's scientific strategic vision and lead problem decomposition and roadmap development.
- Frame research challenges and develop novel methodologies where needed.
- Design scientifically complex software solutions, write critical-path code, and deploy novel models into production.
- Develop reusable science components and drive adoption of state-of-the-art techniques.
- Communicate with stakeholders, foster collaboration across scientists, and support the development of others.
Generated from the employer's posting. Verify important details before applying.
View full postingQualifications
Required
- PhD in operations research, applied mathematics, theoretical computer science, or equivalent, or Master's degree and 4+ years of machine learning, statistical modeling, data mining, and analytics techniques experience
- Knowledge of supply chain management concepts - forecasting, planning, sourcing, optimization and logistics or equivalent
- Experience building and deploying machine learning or optimization models for business applications at scale
- Proficiency in Python with experience developing production-quality scientific software
- Technical depth in one or more of: mathematical optimization, causal inference, sequential decision-making (RL/MDP), or stochastic modeling
Preferred
- Publication record in operations research, machine learning, or a related quantitative field
- Experience with optimization solvers (e.g., Gurobi, OR-Tools) or reinforcement learning frameworks (e.g., RLlib, Stable Baselines)
- Experience with deep learning frameworks (e.g., PyTorch, TensorFlow)
- Experience designing and analyzing experiments in operational or supply chain settings
Original job description
Content provided by the employer
Original job description
Content provided by the employer
Our goals are to increase reliable access to supply, improve supply chain-driven vendor experience, and reduce end-to-end supply chain costs, all in service of maximizing Long-Term Free Cash Flow (LTFCF) for Amazon.
As an Applied Scientist, you will work with software engineers, product managers, and business teams to understand the business problems and requirements, distill that understanding to crisply define the problem, and design and develop innovative solutions to address them.
Our team is highly cross-functional and employs a wide array of scientific tools and techniques to solve key challenges, including optimization, causal inference, and machine learning/deep learning. Some critical research areas in our space include modeling buying decisions under high uncertainty, vendors' behavior and incentives, supply risk and enhancing visibility and reliability of inbound signals.
Key job responsibilities
- Set the scientific strategic vision for the team. Lead problem decomposition and roadmap development.
- Identify and frame research challenges in ambiguous problem areas; invent novel methodologies to address them. Distinguish between problems requiring novel solutions versus those addressable with existing approaches.
- Exercise sound judgment to prioritize between short-term vs. long-term and business vs. technology needs.
- Set an example with exemplary scientific analyses; maintainable, well-tested code; and simple, effective solutions.
- Drive the design of scientifically-complex software solutions, personally writing critical-path code that embodies scientific novelty. Deploy novel models into production with a track record of impactful delivery.
- Develop reusable science components that resolve architecture deficiencies; set standards and drive adoption of state-of-the-art techniques.
- Influence team business and engineering strategies. - Communicate effectively with stakeholders to drive alignment and build consensus.
- Foster collaborations between scientists across Amazon researching similar problems. Proactively resolve endemic issues where the team's technologies bottleneck other teams.
- Actively engage in the development of others, both within and outside the team.
- Participate in the science hiring process and engage with the broader scientific community through publications, presentations, and patents.
Basic Qualifications
- PhD in operations research, applied mathematics, theoretical computer science, or equivalent, or Master's degree and 4+ years of machine learning, statistical modeling, data mining, and analytics techniques experience- Knowledge of supply chain management concepts - forecasting, planning, sourcing, optimization and logistics or equivalent
- Experience building and deploying machine learning or optimization models for business applications at scale
- Proficiency in Python with experience developing production-quality scientific software
- Technical depth in one or more of: mathematical optimization, causal inference, sequential decision-making (RL/MDP), or stochastic modeling
Preferred Qualifications
- Publication record in operations research, machine learning, or a related quantitative field- Experience with optimization solvers (e.g., Gurobi, OR-Tools) or reinforcement learning frameworks (e.g., RLlib, Stable Baselines)
- Experience with deep learning frameworks (e.g., PyTorch, TensorFlow)
- Experience designing and analyzing experiments in operational or supply chain settings
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, Bellevue - 142,800.00 - 193,200.00 USD annually
About the company
amazon
Large Enterprise
Amazon is a global leader in e-commerce and cloud computing, founded in 1994 by Jeff Bezos. Initially starting as an online bookstore, it has since expanded its offerings to include a vast range of products and services, including electronics, fashion, and digital content. With Amazon Web Services (AWS), the company also provides powerful cloud solutions to businesses around the world. Known for its innovation, customer-centric approach, and commitment to operational efficiency, Amazon continues to shape the future of retail and technology, consistently seeking new ways to enhance customer experiences.