About this role
Summary: Builds production machine learning models and causal evaluation frameworks that predict workforce outcomes and recommend targeted actions. Works with economists and engineers to measure the causal impact of programs, products, and policies.
The Central Science Team within Amazon’s People Experience and Technology org (PXTCS) uses economics, behavioral science, statistics, and machine learning to proactively identify mechanisms and process improvements which simultaneously improve Amazon and the lives, well-being, and the value of work to Amazonians. We are an interdisciplinary team, which combines the talents of science and engineering to develop and deliver solutions that measurably achieve this goal.
We are looking for an Applied Scientist to build production machine learning solutions that improve employee experiences and inform workforce decisions. You will work on problems spanning predictive modeling, natural language processing, recommendation systems, personalization, generative AI, and causal evaluation. These solutions will predict workforce outcomes, identify individual and organizational needs, recommend targeted actions, and measure the causal impact of programs, products, and policies across diverse employee populations and business contexts.
As an Applied Scientist, you will develop models and algorithms from prototype through production. You will collaborate with other scientists and economists to combine machine learning with causal inference and experimentation, allowing teams to understand both what is likely to happen and which actions will improve outcomes. You will also work with product managers, engineers, and business partners to translate business needs into technical solutions and communicate methods and results to technical and nontechnical audiences.
Key job responsibilities
• Design, develop, and deploy machine learning solutions in production environments.
• Develop solutions using techniques that include predictive modeling, deep learning, natural language processing, recommendation systems, personalization, and generative AI.
• Build models that predict workforce outcomes, identify employee and organizational needs, and recommend targeted actions.
• Partner with economists and other scientists to design causal evaluations and experimental frameworks that measure the impact of programs, products, and policies.
• Combine predictive models with causal estimates to help business partners identify effective actions and make evidence-based decisions.
• Collaborate with product managers, engineers, and business partners to translate business requirements into modeling approaches and contribute to technical design and roadmap decisions.
• Evaluate model accuracy, performance, calibration, and limitations, and identify opportunities for improvement.
• Communicate technical concepts, methods, results, and recommendations to technical peers and business partners.
A day in the life
• Analyze data to investigate model performance or understand an unexpected result from a causal evaluation.
• Brainstorm modeling and evaluation approaches with applied scientists and economists on the team.
• Develop and test model features, recommendation methods, or generative AI applications.
• Design or analyze experiments and observational studies to estimate the causal impact of programs, products, or policies.
• Run simulations, offline evaluations, or backtests to assess model performance.
• Meet with product, engineering, and business partners to review requirements, data, designs, and implementation decisions.
• Review code changes or technical design documents from scientists and engineers.
• Write and present documents covering methods, results, limitations, and recommendations.
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.
Basic qualifications
- 3+ years of building models for business application experience
- Experience programming in Java, C++, Python or related language
- PhD, or Master's degree and 4+ years of applied research experience
Preferred qualifications
- Experience in professional software development
- Experience in building machine learning models for business application
- Experience in designing experiments and statistical analysis of results
Amazon is an equal opportunity employer and does not discriminate on the basis of protected veteran status, disability, or other legally protected status.
Los Angeles County applicants: Job duties for this position include: work safely and cooperatively with other employees, supervisors, and staff; adhere to standards of excellence despite stressful conditions; communicate effectively and respectfully with employees, supervisors, and staff to ensure exceptional customer service; and follow all federal, state, and local laws and Company policies. Criminal history may have a direct, adverse, and negative relationship with some of the material job duties of this position. These include the duties and responsibilities listed above, as well as the abilities to adhere to company policies, exercise sound judgment, effectively manage stress and work safely and respectfully with others, exhibit trustworthiness and professionalism, and safeguard business operations and the Company’s reputation. Pursuant to the Los Angeles County Fair Chance Ordinance, we will consider for employment qualified applicants with arrest and conviction records.
Pursuant to the San Francisco Fair Chance Ordinance, we will consider for employment qualified applicants with arrest and conviction records.
Our inclusive culture empowers Amazonians to deliver the best results for our customers. If you have a disability and need a workplace accommodation or adjustment during the application and hiring process, including support for the interview or onboarding process, please visit https://amazon.jobs/content/en/how-we-hire/accommodations for more information. If the country/region you’re applying in isn’t listed, please contact your Recruiting Partner.
The base salary range for this position is listed below. Your Amazon package will include sign-on payments and restricted stock units (RSUs). Final compensation will be determined based on factors including experience, qualifications, and location. Amazon also offers comprehensive benefits including health insurance (medical, dental, vision, prescription, Basic Life & AD&D insurance and option for Supplemental life plans, EAP, Mental Health Support, Medical Advice Line, Flexible Spending Accounts, Adoption and Surrogacy Reimbursement coverage), 401(k) matching, paid time off, and parental leave. Learn more about our benefits at https://amazon.jobs/en/benefits.
USA, CA, San Francisco - 171,600.00 - 222,200.00 USD annually
USA, MA, Boston - 142,800.00 - 193,200.00 USD annually
USA, VA, Arlington - 142,800.00 - 193,200.00 USD annually
USA, WA, Bellevue - 142,800.00 - 193,200.00 USD annually
USA, WA, Seattle - 142,800.00 - 193,200.00 USD annually