Applied Scientist - Production ML for Workforce Impact

Amazon Inc.; Boston (MA); On-site; USD 143,000 - 193,000; Full time

Job summary

Amazon.com Services LLC in Boston is seeking an Applied Scientist to build production machine learning solutions that improve employee experiences and inform workforce decisions. You will work on predictive modeling, natural language processing, recommendation systems, personalization, and generative AI.

You will collaborate with economists, product managers, engineers, and business partners to translate needs into modeling approaches, evaluate models, and communicate results to both technical

Qualifications

• 3+ years building models for business applications.
• Experience programming in Java, C++, Python or related language.
• PhD or Masters with 4+ years of applied research experience.

Responsibilities

• Design, develop, and deploy ML solutions in production environments.
• Develop models using predictive modeling, deep learning, NLP, recommender systems.
• Build models predicting workforce outcomes and recommending actions.
• Design causal evaluations and experiments to measure program impact.
• Combine predictive models with causal estimates for evidence-based decisions.
• Collaborate with product, engineering, and business teams on modeling approaches.
• Evaluate model accuracy, performance, calibration, and limitations.
• Communicate methods, results and recommendations to technical and business audiences.

Skills

3+ years of building models for бизнес

Java, C++, Python experience

Education

PhD, or Master's degree and 4+ years of applied research experience

Job description

Amazon.com Services LLC in Boston is seeking an Applied Scientist to build production machine learning solutions that improve employee experiences and inform workforce decisions. You will work on predictive modeling, natural language processing, recommendation systems, personalization, and generative AI.

You will collaborate with economists, product managers, engineers, and business partners to translate needs into modeling approaches, evaluate models, and communicate results to both technical