Principal Research Scientist, Amazon Connect

AWS Applied AI Solutions (AAIS) is building toward a future where every business innovates with Amazon AI teammates. To get there we build AI solutions that improve human capabilities and transform entire business functions. We create end-to-end products that surprise and delight out-of-the-box making complex things easy and hard things possible with no cloud experience required. We start with customers who embrace the future and build bridges to meet the rest where they are. We pursue ambitious opportunities with conviction and we are looking for builders who share that mindset.

Do you want to define the scientific direction for how a global contact-center network forecasts demand schedules its workforce and optimizes operations in real time The Eliza team within Amazon Connect (FCS) is looking for a Principal Research Scientist with a deep operations research specialization to set the research agenda in operations science combinatorial optimization queueing theory stochastic modeling and forecasting and to translate that research into production systems that serve millions of customer interactions.

As a Principal Research Scientist you will be the senior technical voice for operations research across the org. You will identify the highest-leverage scientific problems architect novel solutions and drive them from research through production deployment. You will not sit apart from the work - you will remain deeply hands-on with data models and systems while raising the scientific bar for scientists and engineers around you. This is an individual-contributor Principal role: your influence comes from technical depth invention and the ability to move business and engineering roadmaps through scientific rigor.

You will operate at the intersection of demand forecasting workforce scheduling and network optimization - applying combinatorial optimization to large-scale scheduling and resource-allocation problems and queueing theory to model contact-center dynamics under uncertainty non-stationarity and competing operational constraints at Internet scale - turning that reasoning into systems that continuously sense predict and optimize.

Key job responsibilities
Set the multi-year research direction for operations research across contact-center demand prediction workforce scheduling and network optimization.
Design and deliver novel algorithms in combinatorial optimization (large-scale scheduling resource allocation integer/constraint programming) and queueing theory (contact-center modeling staffing under stochastic arrivals) alongside stochastic modeling and time-series forecasting that advance the state of the art while solving real operational problems.
Own end-to-end scientific solutions from problem formulation and prototyping to production deployment ensuring robustness explainability and seamless integration with existing systems.
Design rigorous experiments and evaluation methodology to validate hypotheses and quantify business impact; establish scientific-excellence mechanisms (metrics benchmarks peer review) that the broader science team adopts.
Partner with engineering product and operations teams to define data and logging requirements get them prioritized on roadmaps and translate scientific capabilities into measurable business outcomes.
Influence senior leadership through written papers and deep-dives framing complex algorithmic trade-offs in clear business terms.
Mentor and raise the bar for applied and research scientists across the org while maintaining significant hands-on technical contribution.
Represent the teams science externally where appropriate (publications patents top-tier venues such as INFORMS NeurIPS ICML).

A day in the life
Your day blends hands-on science with technical leadership. You might spend the morning deep in data and models prototyping a new combinatorial-optimization formulation for workforce scheduling or a queueing model for staffing under stochastic arrivals against production infrastructure and the afternoon guiding fellow scientists through a hard optimization or stochastic-modeling problem reviewing an experiment design or aligning engineering partners on the data architecture needed to unlock the next capability. Youll drive technical discussions with the team and key stakeholders and periodically write and present papers that shape the business and engineering roadmap.

About the team
Amazon values diverse experiences. Even if you do not meet all of the preferred qualifications and skills listed in the job description we encourage candidates to apply. If your career is just starting hasnt followed a traditional path or includes alternative experiences dont let it stop you from applying.

Amazon Web Services (AWS) is the worlds most comprehensive and broadly adopted cloud platform. We pioneered cloud computing and never stopped innovating thats why customers from the most successful startups to Global 500 companies trust our robust suite of products and services to power their businesses.

We value work-life harmony. Achieving success at work should never come at the expense of sacrifices at home which is why flexible work hours and arrangements are part of our culture. When we feel supported in the workplace and at home theres nothing we cant achieve in the cloud.

Here at AWS its in our nature to learn and be curious. Our employee-led affinity groups foster a culture of inclusion that empower us to be proud of our differences. Ongoing events and learning experiences including our Conversations on Race and Ethnicity and AmazeCon conferences inspire us to never stop embracing our uniqueness.

Were continuously raising our performance bar as we strive to become Earths Best Employer. Thats why youll find endless knowledge-sharing mentorship and other career-advancing resources here to help you develop into a better-rounded professional.

- 10 years of tech industry or equivalent experience
- PhD in a quantitative discipline such as statistics mathematics economics computer science or any related quantitative field
- Experience working effectively with science data processing and software engineering teams

- PhD in operations research applied mathematics theoretical computer science or equivalent or experience in at least one of the related science disciplines (optimization - LP MIP statistics machine learning process control combinatorial optimization)
- Experience in leading large-scale technical or engineering programs with a proven record of thought leadership business case development realizing customer benefits and successful program completion
- Experience scripting in modern programming languages or experience with training and deploying machine learning systems to solve large-scale optimizations
- Patents or publications at top-tier peer-reviewed conferences or journals.

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 for more information. If the country/region youre applying in isnt 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 NY New York - 208300.00 - 281800.00 USD annually