At a Glance
- Tasks: Design and build machine learning systems to automate recruitment processes at Amazon.
- Company: Join Amazon's innovative team focused on transforming talent acquisition.
- Benefits: Competitive salary, diverse work environment, and opportunities for professional growth.
- Other info: Collaborative team culture with mentorship and knowledge sharing.
- Why this job: Make a real impact by helping job seekers find their ideal roles.
- Qualifications: Experience in machine learning, programming, and strong analytical skills required.
The predicted salary is between 60000 - 80000 € per year.
Do you want to make a real difference to real people's lives? Want to design and build fair and explainable systems which automate recruitment processes across Amazon? Come and be part of a team that develops new machine learning (ML) technologies, which help Amazon scale for its customers by recruiting diverse teams. Join our Recommendations team within Intelligent Talent Acquisition (ITA) where you’ll build machine learning products that transform how job seekers find opportunities and recruiters discover talent.
You’ll develop sophisticated recommendation systems powering both Amazon Jobs and internal hiring platforms, operating at global scale to match the right people with the right positions. Using techniques including representation learning, reinforcement learning, and probabilistic modeling, your work will directly improve efficiency for recruiters and help candidates find their ideal roles. This position offers the chance to solve complex problems with significant impact by creating systems that make Amazon’s entire hiring ecosystem more effective while collaborating with scientists across the organization.
Key Job Responsibilities
- Design and implement machine learning models that power recommendation systems for job seekers and recruiters, ensuring high performance, scalability, and reliability at global scale.
- Collaborate with engineers, scientists, and product managers to define requirements, create solutions, and deliver products that improve the hiring experience.
- Participate in the full software development lifecycle including scoping, design, coding, testing, documentation, deployment, and maintenance of recommendation systems and ML models.
- Solve complex ML problems using optimal data structures and algorithms, making thoughtful trade-offs between efficiency and maintainability.
- Stay current with scientific literature and develop novel approaches that address business challenges in talent acquisition.
- Provide feedback on scientific work across the organization helping the entire Intelligent Talent Acquisition organization improve.
Day in the Life
You might spend the morning reviewing a colleague’s code for a new recommendation algorithm feature, then collaborate with product managers to refine requirements for an upcoming enhancement. After lunch, you’ll dive into model development, analyzing performance metrics from recent A/B tests and implementing improvements to the job-seeker recommendation pipeline. Throughout the day, you’ll participate in scientific discussions with peers across the organization, providing valuable feedback while continuing to refine your expertise.
About the Team
The Recommendations team is a hybrid group of software engineers and applied scientists located in Edinburgh. We build tools that match people to jobs and jobs to people, optimizing experiences for both recruiters and candidates. Our work directly impacts Amazon’s ability to find and hire exceptional talent globally. The team maintains a collaborative environment with regular knowledge sharing and mentorship opportunities. We work closely with our product teams to understand business needs and develop innovative scientific solutions that improve hiring outcomes across both industry and student requisitions worldwide.
Basic Qualifications
- 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.
- Strong analytical skills, attention to detail, and effective communication abilities.
Preferred Qualifications
- 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.
Amazon is an equal opportunities employer. We believe passionately that employing a diverse workforce is central to our success. We make recruiting decisions based on your experience and skills. We value your passion to discover, invent, simplify and build. Protecting your privacy and the security of your data is a longstanding top priority for Amazon.
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 our website for more information.
Applied Scientist, Intelligent Talent Acquisition - Lead Generation & Detection Services employer: Amazon Science
At Amazon, we are committed to fostering a diverse and inclusive work environment where innovation thrives. As an Applied Scientist in our Edinburgh-based Intelligent Talent Acquisition team, you will have the opportunity to work on cutting-edge machine learning technologies that directly impact recruitment processes, all while enjoying a collaborative culture that prioritises mentorship and professional growth. With access to global resources and a focus on fairness and explainability in ML systems, you'll be part of a mission-driven team dedicated to making a meaningful difference in people's lives.
StudySmarter Expert Advice🤫
We think this is how you could land Applied Scientist, Intelligent Talent Acquisition - Lead Generation & Detection Services
✨Tip Number 1
Network like a pro! Reach out to people in the industry, especially those at Amazon. A friendly chat can lead to insider info about job openings and even referrals, which can give you a leg up in the application process.
✨Tip Number 2
Prepare for interviews by brushing up on your ML knowledge and coding skills. Practice common interview questions related to machine learning and be ready to discuss your past projects. We want to see your passion and expertise shine through!
✨Tip Number 3
Showcase your work! If you've got projects or research that demonstrate your skills in machine learning, make sure to highlight them. A portfolio can really set you apart and show us what you're capable of.
✨Tip Number 4
Don’t forget to apply through our website! It’s the best way to ensure your application gets seen. Plus, keep an eye on our careers page for new opportunities that match your skills and interests.
We think you need these skills to ace Applied Scientist, Intelligent Talent Acquisition - Lead Generation & Detection Services
Some tips for your application 🫡
Tailor Your Application:Make sure to customise your CV and cover letter for the Applied Scientist role. Highlight your experience with machine learning and any relevant projects that showcase your skills in designing algorithms and statistical analysis.
Showcase Your Passion:We want to see your enthusiasm for fair and explainable ML systems! Share examples of how you've tackled complex problems in your previous roles and how you stay updated with the latest scientific literature.
Be Clear and Concise:When writing your application, keep it straightforward. Use clear language to describe your experiences and achievements, making it easy for us to see how you fit into our team and the impact you can make.
Apply Through Our Website:Don’t forget to submit your application through our official website! This ensures that your application is processed correctly and gives you the best chance to join our amazing team at StudySmarter.
How to prepare for a job interview at Amazon Science
✨Know Your ML Stuff
Brush up on your machine learning concepts, especially around recommendation systems. Be ready to discuss your experience with statistical analysis and model building, as well as any specific algorithms you've worked with. This will show that you’re not just familiar with the theory but can apply it practically.
✨Show Your Collaborative Spirit
Since this role involves working closely with engineers, scientists, and product managers, be prepared to share examples of how you've successfully collaborated in the past. Highlight your communication skills and how you’ve contributed to team projects, as teamwork is key in this environment.
✨Prepare for Technical Questions
Expect to tackle some technical questions or coding challenges during the interview. Practice solving problems using optimal data structures and algorithms. You might even want to brush up on Java, C++, or Python, as these are relevant languages for the role.
✨Demonstrate Your Passion for Fairness
The job description emphasises fairness and explainability in ML systems. Be ready to discuss why these aspects are important to you and how you’ve incorporated them into your work. Showing that you care about the ethical implications of your work can set you apart from other candidates.
