Applied Scientist II - Marketing Science & Lead Scoring

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

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.