Applied Scientist, Intelligent Talent Acquisition - Lead Generation & Detection Services

Amazon Amazon · Big Tech · MLN, United Kingdom +1 · Research Science

The role involves designing and implementing machine learning models for recommendation systems to improve Amazon's talent acquisition processes. It focuses on building ML products that match job seekers with opportunities and recruiters with talent, operating at a global scale. The work involves representation learning, reinforcement learning, and probabilistic modeling, with an emphasis on fairness and explainability.

What you'd actually do

  1. Design and implement machine learning models that power recommendation systems for job seekers and recruiters, ensuring high performance, scalability, and reliability at global scale.
  2. Collaborate with engineers, scientists, and product managers to define requirements, create solutions, and deliver products that improve the hiring experience.
  3. Participate in the full software development lifecycle including scoping, design, coding, testing, documentation, deployment, and maintenance of recommendation systems and ML models.
  4. Solve complex ML problems using optimal data structures and algorithms, making thoughtful trade-offs between efficiency and maintainability.
  5. Stay current with scientific literature and develop novel approaches that address business challenges in talent acquisition.

Skills

Required

  • Experience in solving business problems through machine learning, data mining and statistical algorithms
  • Experience programming in Java, C++, Python or related language
  • Speak, write, and read fluently in English
  • Experience that includes strong analytical skills, attention to detail, and effective communication abilities

Nice to have

  • PhD in computer science, machine learning, engineering, or related fields
  • Experience in designing experiments and statistical analysis of results
  • Experience in patents or publications at top-tier peer-reviewed conferences or journals

What the JD emphasized

  • fair and explainable systems
  • fairness and explainability in ML systems

Other signals

  • recommendation systems
  • machine learning products
  • global scale
  • fairness and explainability