Applied Scientist, MOP (Modeling and Optimization Planning)

Amazon; Bellevue, Northern (WA, KY); Hybrid; USD 143,000 - 193,000; Full time

Benefits offered by this job

Medical, Dental, and Vision Coverage

Paid Time Off (PTO)

401(k) Plan

Job summary

Amazon is seeking an Applied Scientist for the MOP team in Bellevue, WA to advance optimization and modeling for labor capacity planning in the Fulfillment Network. You will collaborate across science, software, product, and operations to deliver scalable solutions.

The role emphasizes building production-ready models, simulations, and experiments, with direct impact on process efficiency and associate experience in a fast-paced environment.

Qualifications

• PhD or Master’s in CS/CE/ML or related field.
• 3+ years building models for business applications.
• Experience programming in Java, C++, Python or related language.
• Experience in algorithms, optimization, data mining, high-performance computing.

Responsibilities

• Develop understanding of operational processes and system architecture.
• Identify improvement opportunities from data and code.
• Create scalable mathematical models for production systems.
• Develop prototypes and simulations for experimentation.
• Advocate for technical solutions with stakeholders.
• Partner with engineers to integrate prototypes into production systems.
• Design experiments and build metrics to track performance.

Skills

Operations research

Optimization

Mathematical modeling

Machine learning

Education

PhD or Master's degree in CS/CE/ML or related field

Tools

Java

C++

Python

Job description

Applied Scientist, MOP (Modeling and Optimization Planning)

Job ID: 10519788 | Amazon.com Services LLC

The Amazon GDS-MOP (modeling, Optimization and Planning) Science team is seeking an exceptional Applied Scientist with strong operations research and optimization expertise to develop production solutions for one of the most complex systems in the world: Amazon's Fulfillment Network labor capacity planning.

At MOP Science, we design, build, and deploy optimization, statistics, machine learning, and GenAI/LLM solutions that power Amazon Labor Planning systems (ALPS) running across Amazon Fulfillment Centers worldwide. We solve a wide range of challenges encountered throughout the network, including labor planning and staffing, pick scheduling, stow guidance, and capacity risk management. We are tasked with developing innovative, scalable, and reliable science-driven production solutions that exceed the published state of the art, enabling systems to run frequently (ranging from every few minutes to every few hours per use case) and continuously across our large-scale network.

Key job responsibilities

As an Applied Scientist, you will collaborate with other scientists, software engineers, product managers, and operations leaders to develop optimization-driven solutions using a variety of tools and observe direct impact on process efficiency and associate experience in the fulfillment network. Key responsibilities include:

• Develop understanding and domain knowledge of operational processes, system architecture and functions, and business requirements
• Deep dive into data and code to identify opportunities for continuous improvement and/or disruptive new approaches
• Develop scalable mathematical models for production systems to derive optimal or near-optimal solutions for existing and new challenges
• Create prototypes and simulations for agile experimentation of devised solutions
• Advocate for technical solutions with business stakeholders, engineering teams, and senior leadership
• Partner with engineers to integrate prototypes into production systems
• Design experiments to test new or incremental solutions launched in production and build metrics to track performance

About the team

Amazon offers a full range of benefits that support you and eligible family members, including domestic partners and their children. Benefits can vary by location, the number of regularly scheduled hours you work, length of employment, and job status such as seasonal or temporary employment. The benefits that generally apply to regular, full-time employees include:

• Medical, Dental, and Vision Coverage
• Maternity and Parental Leave Options
• Paid Time Off (PTO)
• 401(k) Plan

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
• Experience programming in Java, C++, Python or related language
• Experience in any of the following areas: algorithms and data structures, parsing, numerical optimization, data mining, parallel and distributed computing, high-performance computing

Preferred Qualifications

• Experience in professional software development

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