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Description

Do you want a role with deep meaning and the ability to make a major impact? As part of Intelligent Talent Acquisition (ITA), you'll have the opportunity to reinvent the hiring process and deliver unprecedented scale, sophistication, and accuracy for Amazon Talent Acquisition operations. ITA is an industry-leading people science and technology organization made up of scientists, engineers, analysts, product professionals and more, all with the shared goal of connecting the right people to the right jobs in a way that is fair and precise. Last year we delivered over 6 million online candidate assessments, and helped Amazon deliver billions of packages around the world by making it possible to hire hundreds of thousands of workers in the right quantity, at the right location and at exactly the right time. You’ll work on state-of-the-art research, advanced software tools, new AI systems, and machine learning algorithms, leveraging Amazon's in-house tech stack to bring innovative solutions to life. Join ITA in using technologies to transform the hiring landscape and make a meaningful difference in people's lives. Together, we can solve the world's toughest hiring problems.

The conversion domain ensures the necessary labor force to fulfill customer orders across global operations. The Senior Applied Scientist serves as the science leader for the conversion domain, supporting leadership teams across North America and Europe while providing guidance to generation, hiring event, and coordination teams.

The Senior Applied Scientist optimizes recruitment processes for specific demands within cost constraints across various markets, each with distinct deadlines. As the technical lead, you own signals that coordinate multiple functions: determining optimal job postings, coordinating with marketing for lead generation, managing hiring event appointments, and implementing candidate management strategies when needed.

The Senior Applied Scientist develops sophisticated theory-based decision models to create a scalable framework that balances business and candidate preferences, extending beyond simple conversion predictions. The role requires careful consideration of local regulations and market-specific requirements.

The Senior Applied Scientist owns scientific models across multiple countries, with expertise in stochastic dynamic programming, machine learning with panel data, survival analysis, and causal inference from non-experimental data. They optimize job application release timing and predict conversion rates while designing experiments for new mechanism rollouts.

The Senior Applied Scientist leads automation of hiring processes through AI systems, developing decision-making models built on economic theory principles while ensuring system trustworthiness through automated reasoning and formal verification.

Key job responsibilities

• Own science models across North America and Europe, that managing job posting optimization, candidate management, and recruitment coordination.

• Create data-driven models that optimize candidate experience and business outcomes while considering market-specific requirements and regulations.

• Manage conversion models, application demand forecasting, and optimization algorithms using machine learning, survival analysis, and causal inference techniques.

• Lead development of AI systems that automate recruitment decisions while ensuring reliability and alignment with business objectives.

• Plan and implement controlled experiments to test new recruitment mechanisms and optimize hiring processes across different markets.

About the team

Intelligent Talent Acquisition (ITA) is Amazon's internal people science and technology organization with the singular mission of connecting the right people to the right jobs through fair and precise methods across Amazon's global operations. This strategic hiring approach directly contributes to Amazon's ability to deliver billions of packages worldwide. Through data-driven insights and innovative technology, ITA continues to enhance Amazon's hiring processes, assessment systems, and talent acquisition capabilities, with scientific breakthroughs affecting thousands of hiring decisions and millions in operational efficiency across 200+ job families globally.

Basic Qualifications

- 3+ years of building machine learning models for business application experience

- PhD, or Master's degree and 6+ years of applied research experience

- Experience programming in Java, C++, Python or related language

- PhD in econometrics, statistics, industrial engineering, operations research, optimization, data mining, analytics, or equivalent quantitative field, or Master's degree

- Experience with data scripting languages (e.g. SQL, Python, R etc.) or statistical/mathematical software (e.g. R, SAS, or Matlab)

- Experience in causal modeling like graphical models, causal Bayesian network, potential outcomes, A/B testing, experiments, quasi-experiments, and data science workflows

- Experience in external enterprise customer-facing role as a technical lead, with strong oral and written communication skills, presenting to both large and small audiences

- 3+ years of experience in applied economics or data science - Demonstrated experience applying economic principles and quantitative methods to solve complex business problems

- Expertise in statistical modeling and machine learning - Proficiency with statistical analysis, predictive modeling, and machine learning techniques for tabular data

Preferred Qualifications

- Experience with modeling tools such as R, scikit-learn, Spark MLLib, MxNet, Tensorflow, numpy, scipy etc.

- Experience with large scale distributed systems such as Hadoop, Spark etc.

- Have peer-reviewed scientific contributions in premier journals and conferences

- Experience working within a global company, with multi-country responsibilities

- Experience in talent acquisition, workforce planning, or labor economics - Background in human resources analytics, hiring optimization, or related people science applications

- Expertise in dynamic optimization and operations research - Advanced knowledge of stochastic optimization, integer programming, and decision-making under uncertainty

- Experience with survival analysis and time-series forecasting - Specialized skills in modeling censored data and predicting future events with temporal dependencies

- Leadership experience managing technical teams - Track record of leading scientists, engineers, or analysts on complex, multi-disciplinary projects

- Experience with AI/ML automation and system reliability - Knowledge of automated decision-making systems, formal verification methods, and ensuring AI trustworthiness

- Cross-functional collaboration experience - Proven ability to work effectively with engineering, operations, marketing, and business teams to drive organizational impact

Amazon is an equal opportunity employer and does not discriminate on the basis of protected veteran status, disability, or other legally protected status.

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.

Our compensation reflects the cost of labor across several US geographic markets. The base pay for this position ranges from $150,400/year in our lowest geographic market up to $260,000/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.