Senior Lead-Scoring ML Scientist | Deep Learning

Amazon; Seattle (WA); On-site; USD 167,100 - 226,100; Full time

Benefits offered by this job

Health insurance

401(k) matching

Paid time off

Parental leave

RSUs

Job summary

Amazon is seeking a Senior Applied Scientist specializing in lead scoring and deep learning. You will design and deploy high-impact models for customer segmentation, adaptive recommendations, and predictive lead/account prioritization.

You’ll work with cross-functional teams to turn research into scalable, production-grade systems. You’ll build end-to-end ML pipelines, prototype advanced models, and publish research while mentoring junior scientists.

Qualifications

• PhD or Master’s degree with 6+ years of applied research experience.
• Experience building ML models for business applications.
• Proficiency in Python and ML frameworks (PyTorch/TensorFlow).
• Strong communication with stakeholders to convey complex ideas.

Responsibilities

• Design and deploy predictive lead scoring models for customer acquisition and retention.
• Architect end-to-end ML pipelines for large-scale deep learning models and real-time inference.
• Publish research and stay ahead of marketing science and propensity modeling trends.
• Innovate in multi-modal modeling for improved scoring across accounts and leads.
• Conduct A/B testing, causal inference, and counterfactual analysis.
• Collaborate with MLOps to deploy, monitor, and retrain models using AWS SageMaker and MLflow.

Skills

Machine learning modeling

Python

PyTorch/TensorFlow

SQL/Spark

Stakeholder communication

Education

PhD or Master’s with extensive research

Tools

PyTorch

TensorFlow

MLflow

SageMaker

Job description

Amazon is seeking a Senior Applied Scientist specializing in lead scoring and deep learning. You will design and deploy high-impact models for customer segmentation, adaptive recommendations, and predictive lead/account prioritization.

You’ll work with cross-functional teams to turn research into scalable, production-grade systems. You’ll build end-to-end ML pipelines, prototype advanced models, and publish research while mentoring junior scientists.