Senior Applied Scientist, Workforce Optimization

Amazon Inc.; Seattle, Northern (WA, KY); Hybrid; USD 190,000 - 230,000; Full time

Benefits offered by this job

Health insurance

Paid time off

RSUs

Job summary

Amazon Inc. in Seattle, WA is seeking a Sr. Applied Scientist to advance Workforce Solutions' ML/AI initiatives.

You will collaborate with Software Engineers, ML Engineers, Data Engineers and product leadership to develop optimization and LLM-based solutions that shape workforce planning and decision making. You will own end-to-end analytics, build scalable models, evaluate business impact, and communicate insights to stakeholders.

Qualifications

• 3+ years of building machine learning models for business applications.
• PhD, or Master's degree and 6+ years of applied research experience.
• Experience programming in Java, C++, Python or related language.

Responsibilities

• Engineering execution - drive crisp and timely execution of milestones, consider and advise on key design and technology trade-offs with engineering teams.
• Priority management - manage diverse requests and dependencies from teams.
• Process improvements - define, implement and continuously improve delivery and operational efficiency.
• Stakeholder management - interface with and influence your stakeholders, balancing business needs vs. technical constraints and driving clarity in ambiguous situations.
• Operational Excellence - monitor metrics and program health, anticipate and clear blockers, manage escalations.

Skills

Machine learning

Analytical thinking

Communication

Education

PhD or MS with 6+ years applied research

Tools

Java

C++

Python

TensorFlow

Job description

Amazon Inc. in Seattle, WA is seeking a Sr. Applied Scientist to advance Workforce Solutions' ML/AI initiatives.

You will collaborate with Software Engineers, ML Engineers, Data Engineers and product leadership to develop optimization and LLM-based solutions that shape workforce planning and decision making. You will own end-to-end analytics, build scalable models, evaluate business impact, and communicate insights to stakeholders.