Location
London

Hours
Full Time

Salary
Competitive, commensurate with experience

About the Role
Do you want a role with deep meaning and the ability to make a major impact? As part of Intelligent Talent Acquisition (ITA) at Amazon, 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, and product professionals, all with the shared goal of connecting the right people to the right jobs in a way that is fair and precise.

Last year, ITA delivered over 6 million online candidate assessments and helped Amazon hire hundreds of thousands of workers in the right quantity, location, and time, enabling the delivery of billions of packages worldwide. ITA is at a critical juncture where growth demands sophisticated analytical capabilities to revolutionize hiring processes while raising the bar for candidate quality.

We are seeking a Level 5 Data Scientist to lead the next generation of Amazon's hiring practices through advanced scientific methods, focusing on System Health Monitoring (SHM) and Select In strategies to optimize candidate quality. This role will drive a projected 60% reduction in defect detection time and contribute to ITA's ambitious 15% efficiency gain target for 2026 through Gen AI-based workflow transformation.

The position is pivotal in transforming our flagship recruiting system monitoring and extending sophisticated anomaly detection solutions across corporate hiring. By implementing advanced anomaly detection and root cause analysis, you will directly impact our ability to provide reliable insights to talent acquisition customers while optimizing quality of hire.

You will lead the development of simulation models to attract and select top-tier talent, raising the bar of candidate quality at Amazon. Integration of cutting-edge Gen AI tools and multi-agent systems will revolutionize scientific workflows, positioning ITA at the forefront of technological innovation in hiring practices.

This role is critical to advancing ITA's primary objective of enhanced candidate evaluation. Without it, system downtime across four critical platforms could compromise candidate quality standards and directly impact Amazon's Quality of Hire strategy.

Key Responsibilities
- Partner with senior Data Scientists and Applied Scientists to identify customer pain points and translate them into measurable goals
- Rapidly iterate prototypes on anomaly detection and agent-based root cause analysis using scientific approaches
- Deploy scalable ML/science-based models using AWS infrastructure
- Proactively identify high-value repetitive work and product opportunities for automation using Gen AI
- Design and run experiments on system stability before and after interventions
- Follow agile workflows for daily project execution
- Collaborate with economists and other Data Scientists to develop integrated strategies for attracting top-tier talent

Experience
- Proven experience as a Data Scientist applying machine learning, statistical modeling, and data analysis tools and techniques
- Experience applying theoretical models in applied environments
- Proficiency with data scripting languages such as SQL, Python, or R, and statistical/mathematical software like R, SAS, or Matlab
- Experience with AWS services including S3, Redshift, SageMaker, EMR, Kinesis, Lambda, and EC2
- Demonstrated ability to apply quantitative analysis to solve business problems and make data-driven decisions

About You
- Strong analytical and problem-solving skills with a passion for innovation
- Ability to communicate complex concepts effectively through written and verbal communication
- Collaborative mindset with experience working in agile teams
- Interest in leveraging Gen AI and advanced scientific methods to transform hiring processes

Qualifications
- Experience defining and creating benchmarks for assessing Gen AI model performance is preferred
- Bachelor’s degree in a quantitative field such as Computer Science, Statistics, Mathematics, or related discipline is typically required
- Advanced degrees or equivalent experience are a plus