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.
ITA stands at a critical juncture where our growth trajectory demands sophisticated analytical capabilities to revolutionize our hiring processes while raising the bar of candidates we hire. We are seeking an L5 Data Scientist to spearhead the next generation of Amazon's hiring practices through advanced scientific methods, focusing on two crucial domains - System Health Monitoring (SHM) and Select In for optimizing candidate quality .The role will be instrumental in driving a projected 60% reduction in defect detection time and contributing to ITA's ambitious 15% efficiency gain with Gen AI based workflow transformation target for 2026.
The position will be 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, this role will directly impact our ability to provide reliable insights to talent acquisition customers while optimizing quality of hire. The role will be leading development of simulation models to identify how to attract and select in talent raising the bar of candidate quality in Amazon. The role will be directly responsible to The integration of cutting-edge Gen AI tools and multi-agent systems will revolutionize our scientific workflows, positioning ITA at the forefront of technological innovation in hiring practices.
The criticality of this Data Scientist role cannot be overstated- Without this position, ITA cannot advance its primary objective of enhanced candidate evaluation, risking system downtime across four critical platforms, compromising candidate quality standards, and directly impacting Amazon's Quality of Hire strategy.
Key job responsibilities The DS will be :
- Partnering with senior DS and Applied scientists to identify customer pain points and transform them to measurable goals
- Iterate rapidly prototypes on anomaly detection, agent based root cause analysis on anomalies detected using scientific approaches
- Deploy scalable ML/science based models using AWS infrastructure
- Identifying proactively areas of high value repeated work /product opportunities as candidates for automation using Gen AI
- Design and run experiments on systems stability pre and post interventions
- Follow agile workflow for daily project execution
- Develop in partnership with economists and other DS an integrated framework for developing strategies that attract top tier talent for Amazon.
Basic Qualifications
- Experience with machine learning/statistical modeling data analysis tools and techniques, and parameters that affect their performance
- Experience applying theoretical models in an applied environment
- Experience working as a Data Scientist
- Experience with data scripting languages (e.g. SQL, Python, R etc.) or statistical/mathematical software (e.g. R, SAS, or Matlab)
- Experience with AWS services including S3, Redshift, Sagemaker, EMR, Kinesis, Lambda, and EC2
- Experience applying quantitative analysis to solve business problems and making data-driven business decisions
Preferred Qualifications
- Experience in defining and creating benchmarks for assessing GenAI model performance
- Experience effectively communicating complex concepts through written and verbal communication
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