Applied Scientist II, GTMC SIERRA

Amazon logo

Amazon

full-time

Posted: January 8, 2025

Number of Vacancies: 1

Job Description

Amazon’s global talent is incredibly complex with unique problems to be solved for each line of business. Global Talent Management (GTM) is centrally responsible for managing and evolving Amazon’s human capital through intelligent talent products and processes. GTM Science is a growing interdisciplinary science team within GTM that develops science products and services to facilitate Amazon’s growth and development of talent across all of our businesses and locations around the world. Our vision in GTM Science is to use machine learning and Generative AI to scalably solve organizational challenges focused on talent movement, talent differentiation, employee-role matching, promotion processes, organizational design and succession planning, diversity and inclusion, and new areas that address the evolving needs of our diverse employee base. We are looking for an experienced ML/AI scientist to work on talent science products that draw from a range of fields such as algorithmic fairness, natural language processing, supervised and unsupervised learning, recommendation systems, machine learning on graphs, reinforcement learning and others on rich and novel datasets. The role has high visibility to senior Amazon business leaders and involves working with other scientists, and partnering with engineering and product teams to integrate these models into production systems. As an applied scientist in GTM Science, you will have the opportunity to work on exciting problems in one of the most innovative applications of science in the People Experience and Technology space. You will help to solve high impact business problems in an unconventional domain, and be encouraged to patent and publish your contributions. If this kind of work excites you, reach out to us to find out more! Key Responsibilities · Design and implement models, science-based product ideas and features, project plans and communicate with stakeholders. · Develop fair predictive models to understand important business and people-centered outcomes · Productionize ML and science models at the scale of Amazon

Locations

  • United States, WA, Seattle, Seattle, WA, 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

  • - 3+ years of building models for business application experienceintermediate
  • - PhD, or Master's degree and 4+ years of CS, CE, ML or related field experienceintermediate
  • - Experience programming in Java, C++, Python or related languageintermediate
  • - Experience in any of the following areas: algorithms and data structures, parsing, numerical optimization, data mining, parallel and distributed computing, high-performance computingintermediate

Required Qualifications

  • - 3+ years of building models for business application experience (experience, 3 years)
  • - PhD, or Master's degree and 4+ years of CS, CE, ML or related field experience (experience, 4 years)
  • - Experience programming in Java, C++, Python or related language (experience)
  • - Experience in any of the following areas: algorithms and data structures, parsing, numerical optimization, data mining, parallel and distributed computing, high-performance computing (experience)

Preferred Qualifications

  • - Experience in investigating, designing, prototyping, and delivering new and innovative system solutions (experience)
  • - Experience in professional software development (experience)
  • - Experience with popular deep learning frameworks such as MxNet and Tensor Flow (experience)
  • - Knowledge of architectural concepts and algorithms, schedule tradeoffs and new opportunities with technical team members (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)

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Machine Learning Science

Applied Scientist II, GTMC SIERRA

Amazon logo

Amazon

full-time

Posted: January 8, 2025

Number of Vacancies: 1

Job Description

Amazon’s global talent is incredibly complex with unique problems to be solved for each line of business. Global Talent Management (GTM) is centrally responsible for managing and evolving Amazon’s human capital through intelligent talent products and processes. GTM Science is a growing interdisciplinary science team within GTM that develops science products and services to facilitate Amazon’s growth and development of talent across all of our businesses and locations around the world. Our vision in GTM Science is to use machine learning and Generative AI to scalably solve organizational challenges focused on talent movement, talent differentiation, employee-role matching, promotion processes, organizational design and succession planning, diversity and inclusion, and new areas that address the evolving needs of our diverse employee base. We are looking for an experienced ML/AI scientist to work on talent science products that draw from a range of fields such as algorithmic fairness, natural language processing, supervised and unsupervised learning, recommendation systems, machine learning on graphs, reinforcement learning and others on rich and novel datasets. The role has high visibility to senior Amazon business leaders and involves working with other scientists, and partnering with engineering and product teams to integrate these models into production systems. As an applied scientist in GTM Science, you will have the opportunity to work on exciting problems in one of the most innovative applications of science in the People Experience and Technology space. You will help to solve high impact business problems in an unconventional domain, and be encouraged to patent and publish your contributions. If this kind of work excites you, reach out to us to find out more! Key Responsibilities · Design and implement models, science-based product ideas and features, project plans and communicate with stakeholders. · Develop fair predictive models to understand important business and people-centered outcomes · Productionize ML and science models at the scale of Amazon

Locations

  • United States, WA, Seattle, Seattle, WA, 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

  • - 3+ years of building models for business application experienceintermediate
  • - PhD, or Master's degree and 4+ years of CS, CE, ML or related field experienceintermediate
  • - Experience programming in Java, C++, Python or related languageintermediate
  • - Experience in any of the following areas: algorithms and data structures, parsing, numerical optimization, data mining, parallel and distributed computing, high-performance computingintermediate

Required Qualifications

  • - 3+ years of building models for business application experience (experience, 3 years)
  • - PhD, or Master's degree and 4+ years of CS, CE, ML or related field experience (experience, 4 years)
  • - Experience programming in Java, C++, Python or related language (experience)
  • - Experience in any of the following areas: algorithms and data structures, parsing, numerical optimization, data mining, parallel and distributed computing, high-performance computing (experience)

Preferred Qualifications

  • - Experience in investigating, designing, prototyping, and delivering new and innovative system solutions (experience)
  • - Experience in professional software development (experience)
  • - Experience with popular deep learning frameworks such as MxNet and Tensor Flow (experience)
  • - Knowledge of architectural concepts and algorithms, schedule tradeoffs and new opportunities with technical team members (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)

Target Your Resume for "Applied Scientist II, GTMC SIERRA"

Get personalized recommendations to optimize your resume specifically for Applied Scientist II, GTMC SIERRA. 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, GTMC SIERRA"

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