Applied Scientist: ML & Causal AI for Workforce Insights

Amazon.com Inc.; San Francisco (CA); On-site; USD 172,000 - 222,000; Full time

Job summary

Amazon.com Services LLC seeks an Applied Scientist to build production machine learning solutions that improve employee experiences and inform workforce decisions in a dynamic, interdisciplinary team. You will work on predictive modeling, NLP, recommender systems, personalization, and generative AI, collaborating with economists and engineers to translate business needs into robust technical solutions.

The role emphasizes learning from causal evaluations, communicating results to diverse

Qualifications

• 3+ years of building models for business applications.
• Experience programming in Java, C++, Python or related language.
• PhD or Master’s degree and 4+ years of applied research experience.

Responsibilities

• Design, develop, and deploy machine learning solutions in production environments.
• Develop solutions using predictive modeling, deep learning, NLP, recommender systems, personalization, and generative AI.
• Build models that predict workforce outcomes and identify needs.
• Partner with economists to design causal evaluations and experimental frameworks.
• Combine predictive models with causal estimates to help actions and decisions.
• Collaborate with product managers, engineers, and business partners to translate requirements into modeling approaches.
• Evaluate model accuracy, calibration, and limitations; identify improvement opportunities.
• Communicate methods, results, and recommendations to technical peers and business partners.

Skills

3+ years building models

Java/C++/Python

Education

PhD or MS + 4+ yrs applied research

Job description

Amazon.com Services LLC seeks an Applied Scientist to build production machine learning solutions that improve employee experiences and inform workforce decisions in a dynamic, interdisciplinary team. You will work on predictive modeling, NLP, recommender systems, personalization, and generative AI, collaborating with economists and engineers to translate business needs into robust technical solutions.

The role emphasizes learning from causal evaluations, communicating results to diverse