Applied Scientist, Tallent Intellgience

Amazon Amazon · Big Tech · Arlington, VA · Human Resources

This role focuses on building and deploying AI systems and machine learning models to optimize Amazon's global campus recruiting process. Key responsibilities include developing models for school recommendations, talent supply projections, and matching candidates to requisitions. The role involves creating data-driven models for sourcing strategies and recruiter decision-making, developing supply forecasting and school ranking algorithms, and leading the development of AI systems for automation and strategy recommendations. It also includes planning and implementing controlled experiments to test and optimize these systems.

What you'd actually do

  1. Own science models that power target school recommendations, talent supply projections, and school-to-requisition matching across Amazon's global campus recruiting footprint.
  2. Create data-driven models that optimize school sourcing strategies and recruiter decision-making while considering diversity requirements, data privacy thresholds, and institution-specific characteristics.
  3. Develop and maintain supply forecasting models, school ranking algorithms, and multi-objective optimization systems using time-series forecasting, machine learning, survival analysis, and causal inference techniques.
  4. Lead development of AI systems to automate school identification, supply-demand gap analysis, and strategy recommendations while ensuring transparency and alignment with recruiting objectives.
  5. Plan and implement controlled experiments to test new recommendation mechanisms, validate supply projection accuracy, and optimize school engagement strategies across different markets and role families.

Skills

Required

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

Nice to have

  • Experience using Unix/Linux
  • Experience in professional software development

What the JD emphasized

  • science models
  • data-driven models
  • supply forecasting models
  • school ranking algorithms
  • multi-objective optimization systems
  • time-series forecasting
  • machine learning
  • survival analysis
  • causal inference
  • AI systems
  • controlled experiments
  • recommendation mechanisms
  • supply projection accuracy
  • school engagement strategies

Other signals

  • AI systems
  • machine learning algorithms
  • science models
  • data-driven models
  • supply forecasting models
  • school ranking algorithms
  • multi-objective optimization systems
  • time-series forecasting
  • machine learning
  • survival analysis
  • causal inference
  • AI systems to automate school identification
  • supply-demand gap analysis
  • strategy recommendations
  • controlled experiments
  • recommendation mechanisms
  • supply projection accuracy
  • school engagement strategies