Applied Scientist

- 2+ years of building models for business application experience
- PhD, or Master's degree and 2+ years of CS, CE, ML or related field experience
- Experience in patents or publications at top-tier peer-reviewed conferences or journals
- 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
- 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, NY, New York - 172,400.00 - 223,400.00 USD annually
USA, TX, Austin - 142,800.00 - 193,200.00 USD annually
USA, VA, Arlington - 142,800.00 - 193,200.00 USD annually
USA, WA, Seattle - 142,800.00 - 193,200.00 USD annually
As an Applied Scientist II specializing in lead scoring and deep learning modeling, you will build and improve machine learning models that power how our business engages with customers. You will develop predictive models for customer segmentation, scoring, and lead/account prioritization, working within an established scoring architecture and collaborating with senior scientists and cross-functional teams to deliver production-grade components.

Key job responsibilities
* Build and iterate on predictive lead scoring models to support customer acquisition, conversion, and retention strategies using techniques such as survival analysis, graph networks, or transformer-based architectures.

* Develop and maintain ML pipeline components for deep learning models, including data preprocessing, feature engineering, model training, and inference integration.

* Contribute to internal and external research, including science reviews, technical publications, and patent filings in collaboration with senior scientists.

* Apply multi-modal modeling techniques (text, graph, behavioral, and temporal data) to enhance scoring accuracy across account and lead levels.

* Conduct A/B testing, causal inference, and counterfactual analysis to measure model impact and iterate on model design.

* Partner with MLOps engineers on model deployment, monitoring, and retraining using tools like AWS SageMaker, MLflow, and other internal tools.

* Participate in science reviews to maintain and raise the quality bar within the team.

* Implement and execute offline and online evaluation frameworks; track success metrics tied to business outcomes (conversion rates, pipeline generation).

About the team
The AWS Marketing Science team builds the ML models and measurement systems that drive marketing decisions across Amazon Web Services. We own incrementality and valuation, ROI measurement, marketing attribution, propensity scoring, account and lead clustering, and next-best-action models. Our work directly influences how AWS allocates marketing spend, targets accounts, and measures effectiveness across billions in pipeline.