Applied Scientist II, People eXperience Technology Central Science (PXTCS)

Amazon logo

Amazon

full-time

Posted: September 24, 2025

Number of Vacancies: 1

Job Description

Do you want to leverage your expertise in machine learning and data science to improve the lives and work of over a million people worldwide? If so, People eXperience Technology Central Science (PXTCS) would love to discuss how you can make that a reality.PXTCS is an interdisciplinary team that uses economics, behavioral science, statistics, and machine learning to identify products, mechanisms, and process improvements that enhance Amazonians' well-being and their ability to deliver value for Amazon's customers. We collaborate with HR teams across Amazon to make Amazon PXT the most scientific human resources organization in the world.Key job responsibilitiesAs an Applied Scientist II, you will be responsible for developing and implementing machine learning solutions across our predictive modeling and forecasting work-streams. You will work on existing models and develop new ones that power leaders across Amazon to make decisions about their businesses. You will collaborate with scientists and engineers to deliver innovative solutions while working closely with business stakeholders to understand their needs.A day in the lifeYou will work across different business domains (corporate, operations, safety) and analysis levels (individual, group, organizational), using various modeling approaches (linear, tree, deep neural network, and LLM-based). You will develop end-to-end ML solutions from problem formulation to deployment, while maintaining high scientific standards and technical excellence.

Locations

  • United States, NY, New York, New York, NY, United States

Salary

Salary not disclosed

Estimated Salary Rangehigh confidence

180,000 - 280,000 USD / yearly

Source: ai estimated

* This is an estimated range based on market data and may vary based on experience and qualifications.

Skills Required

  • - 5+ years of solving business problems through machine learning, data mining and statistical algorithms experienceintermediate
  • - PhD, or a Master's degree and experience in CS, CE, ML or related field researchintermediate
  • - Strong programming skills in Python and experience with ML frameworksintermediate
  • - Experience with machine learning algorithms and statistical analysisintermediate

Required Qualifications

  • - 5+ years of solving business problems through machine learning, data mining and statistical algorithms experience (experience, 5 years)
  • - PhD, or a Master's degree and experience in CS, CE, ML or related field research (experience)
  • - Strong programming skills in Python and experience with ML frameworks (experience)
  • - Experience with machine learning algorithms and statistical analysis (experience)
  • - Strong analytical and problem-solving skills (experience)
  • - Excellent verbal and written communication skills (experience)

Preferred Qualifications

  • - Experience in state-of-the-art deep learning models architecture design and deep learning training and optimization and model pruning (experience)
  • - Have publications at top-tier peer-reviewed conferences or journals (experience)
  • - Experience in predictive modeling, forecasting, and causal inference (experience)
  • - Experience with cloud computing platforms (AWS preferred) (experience)
  • Our compensation reflects the cost of labor across several US geographic markets. The base pay for this position ranges from $136,000/year in our lowest geographic market up to $223,400/year in our highest geographic market. Pay is based on a number of factors including market location and may vary depending on job-related knowledge, skills, and experience. Amazon is a total compensation company. Dependent on the position offered, equity, sign-on payments, and other forms of compensation may be provided as part of a total compensation package, in addition to a full range of medical, financial, and/or other benefits. For more information, please visit https://www.aboutamazon.com/workplace/employee-benefits. This position will remain posted until filled. Applicants should apply via our internal or external career site. (experience)

Responsibilities

  • As an Applied Scientist II, you will be responsible for developing and implementing machine learning solutions across our predictive modeling and forecasting work-streams. You will work on existing models and develop new ones that power leaders across Amazon to make decisions about their businesses. You will collaborate with scientists and engineers to deliver innovative solutions while working closely with business stakeholders to understand their needs.

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Machine Learning Science

Applied Scientist II, People eXperience Technology Central Science (PXTCS)

Amazon logo

Amazon

full-time

Posted: September 24, 2025

Number of Vacancies: 1

Job Description

Do you want to leverage your expertise in machine learning and data science to improve the lives and work of over a million people worldwide? If so, People eXperience Technology Central Science (PXTCS) would love to discuss how you can make that a reality.PXTCS is an interdisciplinary team that uses economics, behavioral science, statistics, and machine learning to identify products, mechanisms, and process improvements that enhance Amazonians' well-being and their ability to deliver value for Amazon's customers. We collaborate with HR teams across Amazon to make Amazon PXT the most scientific human resources organization in the world.Key job responsibilitiesAs an Applied Scientist II, you will be responsible for developing and implementing machine learning solutions across our predictive modeling and forecasting work-streams. You will work on existing models and develop new ones that power leaders across Amazon to make decisions about their businesses. You will collaborate with scientists and engineers to deliver innovative solutions while working closely with business stakeholders to understand their needs.A day in the lifeYou will work across different business domains (corporate, operations, safety) and analysis levels (individual, group, organizational), using various modeling approaches (linear, tree, deep neural network, and LLM-based). You will develop end-to-end ML solutions from problem formulation to deployment, while maintaining high scientific standards and technical excellence.

Locations

  • United States, NY, New York, New York, NY, United States

Salary

Salary not disclosed

Estimated Salary Rangehigh confidence

180,000 - 280,000 USD / yearly

Source: ai estimated

* This is an estimated range based on market data and may vary based on experience and qualifications.

Skills Required

  • - 5+ years of solving business problems through machine learning, data mining and statistical algorithms experienceintermediate
  • - PhD, or a Master's degree and experience in CS, CE, ML or related field researchintermediate
  • - Strong programming skills in Python and experience with ML frameworksintermediate
  • - Experience with machine learning algorithms and statistical analysisintermediate

Required Qualifications

  • - 5+ years of solving business problems through machine learning, data mining and statistical algorithms experience (experience, 5 years)
  • - PhD, or a Master's degree and experience in CS, CE, ML or related field research (experience)
  • - Strong programming skills in Python and experience with ML frameworks (experience)
  • - Experience with machine learning algorithms and statistical analysis (experience)
  • - Strong analytical and problem-solving skills (experience)
  • - Excellent verbal and written communication skills (experience)

Preferred Qualifications

  • - Experience in state-of-the-art deep learning models architecture design and deep learning training and optimization and model pruning (experience)
  • - Have publications at top-tier peer-reviewed conferences or journals (experience)
  • - Experience in predictive modeling, forecasting, and causal inference (experience)
  • - Experience with cloud computing platforms (AWS preferred) (experience)
  • Our compensation reflects the cost of labor across several US geographic markets. The base pay for this position ranges from $136,000/year in our lowest geographic market up to $223,400/year in our highest geographic market. Pay is based on a number of factors including market location and may vary depending on job-related knowledge, skills, and experience. Amazon is a total compensation company. Dependent on the position offered, equity, sign-on payments, and other forms of compensation may be provided as part of a total compensation package, in addition to a full range of medical, financial, and/or other benefits. For more information, please visit https://www.aboutamazon.com/workplace/employee-benefits. This position will remain posted until filled. Applicants should apply via our internal or external career site. (experience)

Responsibilities

  • As an Applied Scientist II, you will be responsible for developing and implementing machine learning solutions across our predictive modeling and forecasting work-streams. You will work on existing models and develop new ones that power leaders across Amazon to make decisions about their businesses. You will collaborate with scientists and engineers to deliver innovative solutions while working closely with business stakeholders to understand their needs.

Target Your Resume for "Applied Scientist II, People eXperience Technology Central Science (PXTCS)"

Get personalized recommendations to optimize your resume specifically for Applied Scientist II, People eXperience Technology Central Science (PXTCS). Our AI analyzes job requirements and tailors your resume to maximize your chances.

Keyword optimization
Skills matching
Experience alignment

Check Your ATS Score for "Applied Scientist II, People eXperience Technology Central Science (PXTCS)"

Find out how well your resume matches this job's requirements. Our Applicant Tracking System (ATS) analyzer scores your resume based on keywords, skills, and format compatibility.

Instant analysis
Detailed feedback
Improvement tips

Documents

Tags & Categories

Machine Learning Science