Applied Scientist , AWS Marketing Science

Amazon Web Services (AWS); New York (NY); On-site; USD 172,000 - 223,000; Full time

Benefits offered by this job

Health insurance

401(k) matching

Parental leave

Paid time off

Job summary

Amazon Web Services seeks an Applied Scientist II specializing in lead scoring and deep learning to build and improve models powering customer engagement. You will develop predictive models for segmentation, scoring, and prioritization within an established scoring architecture, collaborating with senior scientists and cross-functional teams to deliver production-grade components.

You will contribute to ML pipelines, perform offline and online evaluations, and apply multi-modal data techniques.

Qualifications

• 2+ years of building models for business applications.
• PhD or Master's degree plus 2+ years in CS/CE/ML or related field.
• Experience in patents or publications at top-tier conferences or journals.

Responsibilities

• Build and iterate on predictive lead scoring models to support acquisition, conversion and retention strategies.
• Develop and maintain ML pipeline components for deep learning models (data preprocessing, feature engineering, training, inference).
• Contribute to internal/external research, including science reviews, publications, and patent filings.
• Apply multi-modal modeling techniques (text, graph, behavioral, temporal data) to enhance scoring accuracy.
• Conduct A/B testing, causal inference, and counterfactual analysis to measure impact.
• Partner with MLOps engineers on deployment, monitoring, and retraining (SageMaker, MLflow).
• Participate in science reviews to raise quality standards.

Skills

Java

C++

Python

Algorithms & data structures

Numerical optimization

Data mining

Parallel & distributed computing

High-performance computing

Publications or patents

Education

PhD

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

Job description

Description

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.

Basic Qualifications

• 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

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.

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

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USA, WA, Seattle - 142,800.00 - 193,200.00 USD annually