AmazonPosted 2d ago

Senior Applied Scientist, Amazon Connect

Senior Applied Scientist, Amazon Connect at Amazon scores 90 out of 100 on AI centrality, which makes it AI Level 4 of 4 (Builds AI) on this board. The level measures how much of the work is AI, not seniority.

US, NY, New Yorkseniorfull-time$184k-$249k

AI in this role

Design and deploy novel machine learning models and algorithms to power contact center operations and optimization in Amazon Connect.

tensorflowscikit-learnmachine-learningpythonaws

machine-learningstochastic-modelingoptimizationanomaly-detection

Are you excited about applying machine learning and statistical modeling to real-world systems that serve millions of customers? Amazon Connect is a cloud-based contact center service that helps businesses deliver personal, efficient customer experiences. Our team of scientists and engineers builds the AI and ML capabilities that power contact center operations and optimization.

We are looking for a Senior Applied Scientist to tackle scientifically complex challenges in areas such as stochastic modeling, queueing theory, anomaly detection, and optimization. In this role, you will design and deploy novel ML models and algorithms that directly improve how businesses interact with their customers. You will work at the intersection of research and production, turning ambiguous problems into scalable solutions that shape the future of cloud-based customer service.

Key job responsibilities
- Design and deploy novel machine learning models and algorithms to solve complex problems in contact center operations, including forecasting, routing optimization, and anomaly detection.
- Lead the scientific agenda for your team by identifying new research opportunities, proposing initiatives, and driving them from concept through production deployment.
- Collaborate with engineering teams to architect and implement scalable ML systems, personally contributing significant portions of the critical scientific components.
- Mentor fellow scientists and engineers through code reviews, design discussions, and scientific guidance, raising the overall technical bar of the team.
- Evaluate and advance the team's ML methodology by benchmarking against current academic and industry research, and by publishing findings internally and externally when appropriate.

A day in the life
You might start your morning reviewing experiment results from a new forecasting model, then join a design session with engineers to discuss how to integrate it into the production pipeline. After lunch, you could be whiteboarding a novel approach to a queueing optimization problem with a fellow scientist, followed by a code review for a teammate. You will regularly present your research findings to stakeholders across the organization and contribute to the team's publication efforts.

About the team
Our team within Amazon Connect focuses on building intelligent, ML-driven capabilities that help businesses run their contact centers more effectively. We work closely with product, engineering, and science partners to turn research ideas into features that customers rely on every day. We value curiosity, collaboration, and scientific rigor, and we are investing in new AI capabilities that will continue to transform the customer service industry. If you want to see your research make a tangible impact at scale, this is the place to do it.

Basic qualifications

- 3+ years of building machine learning models for business application experience
- PhD, or Master's degree and 6+ years of applied research experience
- Experience programming in Java, C++, Python or related language
- Experience with neural deep learning methods and machine learning

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. 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, NY, New York - 183,800.00 - 248,700.00 USD annually

Prepare for this job

A free preview built only from this posting: what it asks for, what you could be asked in an interview, and how to adjust your resume.

Skills and AI tools this role asks for

Machine LearningStochastic ModelingOptimizationAnomaly DetectionTensorFlowscikit-learnPythonAws

Questions you could be asked

Tell me about a project where machine learning was part of your work. What did you do?
Tell me about a project where stochastic modeling was part of your work. What did you do?
Tell me about a project where optimization was part of your work. What did you do?
Tell me about a project where anomaly detection was part of your work. What did you do?
Walk me through how you've used TensorFlow in your day-to-day work.

Adapt your resume

List these exact terms on your resume: Machine Learning, Stochastic Modeling, Optimization, Anomaly Detection, and TensorFlow. 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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