Applied Scientist II - Lead Scoring & Deep Learning

Amazon Web Services (AWS); Seattle (WA); On-site; USD 143,000 - 193,000; Full time

Job summary

Amazon Web Services (AWS) is seeking an Applied Scientist II to build predictive lead scoring models and deep learning-based customer segmentation. You will develop production-grade components within an established scoring architecture, collaborating with senior scientists and cross-functional teams.

You will contribute to research, apply multi-modal data, and drive evaluation with offline and online experiments, while partnering with MLOps to deploy and monitor models in production.

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.

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).

Skills

Modeling experience

PhD or MS+

Publications

Java/C++/Python

Algorithms

Data mining

HPC

Education

PhD or Master’s degree

Tools

Java/C++/Python

Job description

Amazon Web Services (AWS) is seeking an Applied Scientist II to build predictive lead scoring models and deep learning-based customer segmentation. You will develop production-grade components within an established scoring architecture, collaborating with senior scientists and cross-functional teams.

You will contribute to research, apply multi-modal data, and drive evaluation with offline and online experiments, while partnering with MLOps to deploy and monitor models in production.