Senior Applied Scientist, Sales Insights & GenAI

Amazon Science; Seattle (WA); On-site; USD 167,000 - 226,000; Full time

Benefits offered by this job

Health insurance

RSUs

401(k) matching

Job summary

Amazon Web Services (AWS) is seeking a Senior Applied Scientist to own high-value ML/GenAI problems for the Sales Insights Analytics and Data Science team. You will frame questions with business leaders, and deliver deployed models that inform AWS Sales strategies and drive adoption across customers.

You will lead with technical direction, mentor scientists, and collaborate with stakeholders to translate results into actionable business decisions, shaping the team’s scientific impact at scale.

Qualifications

• PhD, or Master’s degree and 6+ years of building ML models for business applications.
• Experience with SQL and Python scripting.
• Strong written and verbal communication to executives and non-technical leaders.
• Experience managing and deploying ML products.

Responsibilities

• Own full lifecycle of complex science problems: framing, data exploration, modeling, evaluation, and deployment.
• Set technical direction on ambiguous problems and make design decisions.
• Partner with Sales to turn business questions into well-scoped science and recommendations.
• Raise the scientific bar through design reviews, mentorship, and guidance.
• Communicate methods, trade-offs, and results to technical and non-technical audiences.
• Use Python, PySpark, and SQL for data analysis and model development.

Skills

ML expertise

Communication

Mentoring

Stakeholder engagement

Production ML

Education

PhD

Master's degree

Tools

SQL

Python

Job description

Amazon Web Services (AWS) is seeking a Senior Applied Scientist to own high-value ML/GenAI problems for the Sales Insights Analytics and Data Science team. You will frame questions with business leaders, and deliver deployed models that inform AWS Sales strategies and drive adoption across customers.

You will lead with technical direction, mentor scientists, and collaborate with stakeholders to translate results into actionable business decisions, shaping the team’s scientific impact at scale.