About the job
The Sales Insights Analytics and Data Science (SIADS) team is looking for a Senior Applied Scientist to take a high-impact, senior technical role on our Data Science team. We build the machine learning and GenAI products that help the AWS Sales organization grow. Our work spans GenAI agents that answer sales questions in natural language, knowledge graphs, recommendation systems, synthetic controls, account prioritization models, and more.
As a Senior Applied Scientist, you will own ambiguous, high-value problems end to end, from framing the question with business leaders through to a deployed model that changes how the field operates. You will set technical direction for problems that don't yet have a clear approach, choose the right method rather than the familiar one, and raise the bar for the science across the team. You will partner directly with customers across AWS Sales to understand the challenges they face and deliver solutions they trust and adopt.
You will do this alongside a team of talented scientists working on some of the hardest and most impactful problems at the fastest-growing cloud provider in the world. You will have significant room to decide where to focus your efforts and to shape the direction of the work.
You must be an expert in advanced quantitative and machine learning methods, and equally strong at synthesizing and communicating insights to audiences of varying technical sophistication. You will influence decisions well beyond your own projects, and you will help grow the scientists around you through mentorship and technical leadership.
Responsibilities
Own the full lifecycle of complex science problems: problem framing, data exploration, method selection, modeling, evaluation, and production deployment.
Set technical direction on ambiguous problems and make the design decisions that others build on.
Partner with business stakeholders across Sales to turn open-ended business questions into well-scoped science, and translate results into recommendations leaders act on.
Raise the scientific bar across the team through design reviews, mentorship, and hands-on guidance.
Communicate methods, trade-offs, and results clearly to both technical and non-technical audiences.
Use Python, PySpark, and SQL for data analysis and model development.
Qualifications
Minimum
PhD, or Master's degree and 6+ years of building machine learning models for business application experience
Experience with SQL and Python scripting
Experience in written and verbal communication with the ability to present complex technical information in a clear and concise manner to executives and non-technical leaders
Experience managing and deploying ML products
Preferred
Experience working with stakeholders, or experience using financial models, KPIs, and data analysis to inform business decisions with proven business impact (e.g., financial savings, operational improvements, or customer benefits)
PhD or equivalent research experience, or a Master's degree and experience in patents or publications at top-tier peer-reviewed conferences or journals
Depth in large language models and agentic system
Experience mentoring or developing other scientists.