Sr. Applied Scientist, PXT Central Science
Amazon
San Francisco, California
Posted 6 days ago
Responsibilities
Primary Duties
- Design and deploy large-scale machine learning systems in production environments
- Develop innovative ML solutions using state-of-the-art techniques including deep learning, NLP, and generative AI
- Create ML solutions that personalize manager onboarding and development experiences identifying individual capability gaps, recommending tailored learning pathways, and measuring development effectiveness across diverse manager populations and contexts
- Partner to build causal inference models and experimental frameworks to measure impact
- Collaborate with product managers, engineers, and business leaders to define technical roadmaps
- Communicate complex technical concepts to diverse audiences, from technical peers to senior leadership
Full Job Description
Senior Applied Scientist
We are seeking a Senior Applied Scientist to build production machine learning systems that solve complex business problems at scale. You will design, develop, and deploy ML solutions that directly impact millions of users and drive strategic decision-making across the organization. In this role, you will work on challenging problems spanning predictive modeling, natural language processing, recommendation systems, and generative AI applications. You will collaborate with cross-functional teams to translate ambiguous business challenges into rigorous technical solutions.
Key job responsibilities:
About the team:
The People eXperience and Technology Central Science Team (PXTCS) uses economics, behavioral science, statistics, and machine learning to proactively identify mechanisms and process improvements which simultaneously improve Amazon and the lives, wellbeing, and the value of work to Amazonians. We are an interdisciplinary team that combines the talents of science and engineering to develop and deliver solutions that measurably achieve this goal.
We are seeking a Senior Applied Scientist to build production machine learning systems that solve complex business problems at scale. You will design, develop, and deploy ML solutions that directly impact millions of users and drive strategic decision-making across the organization. In this role, you will work on challenging problems spanning predictive modeling, natural language processing, recommendation systems, and generative AI applications. You will collaborate with cross-functional teams to translate ambiguous business challenges into rigorous technical solutions.
Key job responsibilities:
- Design and deploy large-scale machine learning systems in production environments
- Develop innovative ML solutions using state-of-the-art techniques including deep learning, NLP, and generative AI
- Create ML solutions that personalize manager onboarding and development experiences identifying individual capability gaps, recommending tailored learning pathways, and measuring development effectiveness across diverse manager populations and contexts
- Partner to build causal inference models and experimental frameworks to measure impact
- Collaborate with product managers, engineers, and business leaders to define technical roadmaps
- Communicate complex technical concepts to diverse audiences, from technical peers to senior leadership
About the team:
The People eXperience and Technology Central Science Team (PXTCS) uses economics, behavioral science, statistics, and machine learning to proactively identify mechanisms and process improvements which simultaneously improve Amazon and the lives, wellbeing, and the value of work to Amazonians. We are an interdisciplinary team that combines the talents of science and engineering to develop and deliver solutions that measurably achieve this goal.
How to Apply
$93
/ hour
Amazon pays $93 for Software Engineer in San Francisco, California, with most salaries ranging from $72 to $125. Pay can vary based on role, experience, and local cost of living.
Median
$93
Low
$72
High
$125
Companies Similar to Amazon for Jobs
Share This Job
Figures represent approximate ranges and may vary based on experience, location, and other factors. For the most accurate information, please consult the employer directly. Contact us to suggest updates to this information.





