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- Tasks: Revolutionise hiring with AI-driven solutions and impactful research.
- Company: Join Amazon's Intelligent Talent Acquisition team, a leader in people science and technology.
- Benefits: Competitive salary, diverse work culture, and opportunities for professional growth.
- Other info: Collaborative environment focused on innovation and solving complex business challenges.
- Why this job: Make a real difference in people's lives by transforming the hiring landscape.
- Qualifications: PhD or Master's in quantitative research; experience with R or Python.
The predicted salary is between 36000 - 60000 £ per year.
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.
You'll work on state-of-the-art research, advanced software tools, new AI systems, and machine learning algorithms, leveraging Amazon's in-house tech stack to bring innovative solutions to life. Join ITA in using technologies to transform the hiring landscape and make a meaningful difference in people's lives. Together, we can solve the world's toughest hiring problems.
A day in the life: As a Research Scientist, you will partner on design and development of AI-powered systems to scale job analyses enterprise-wide, match potential candidates to the jobs they'll be most successful in, and conduct validation research for top-of-funnel AI-based evaluation tools. You'll have the opportunity to develop and implement novel research strategies using the latest technology and to build solutions while experiencing Amazon's customer-focused culture. The ideal scientist must have the ability to work with diverse groups of people and inter-disciplinary cross-functional teams to solve complex business problems.
About the team: The Lead Generation & Detection Services (LEGENDS) organization is a specialized organization focused on developing AI-driven solutions to enable fair and efficient talent acquisition processes across Amazon. Our work encompasses capabilities across the entire talent acquisition lifecycle, including role creation, recruitment strategy, sourcing, candidate evaluation, and talent deployment. The focus is on utilizing state-of-the-art solutions using Deep Learning, Generative AI, and Large Language Models (LLMs) for recruitment at scale that can support immediate hiring needs as well as longer-term workforce planning for corporate roles. We maintain a portfolio of capabilities such as job-person matching, person screening, duplicate profile detection, and automated applicant evaluation, as well as a foundational competency capability used throughout Amazon to help standardize the assessment of talent interested in Amazon.
Basic Qualifications:
- PhD, or a Master's degree and experience in quantitative field research
- Knowledge of R or Python
- Experience communicating qualitative research methods and findings to non-qualitative researchers
- Experience investigating the feasibility of applying scientific principles and concepts to business problems and products
- Experience in applied selection research, job analysis, test development, and validation
Preferred Qualifications:
- Experience converting research studies into tangible real-world changes
- Knowledge of AWS platforms such as S3, Glue, Athena, Sagemaker
- Experience with big data technologies such as AWS, Hadoop, Spark, Pig, Hive etc.
- PhD in Industrial/Organizational Psychology or related field
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 https://amazon.jobs/content/en/how-we-hire/accommodations for more information.
Senior Research Scientist, Intelligent Talent Acquisition - Lead Generation & Detection Services in Edinburgh employer: 55 Redefined Ltd
Contact Detail:
55 Redefined Ltd Recruiting Team
StudySmarter Expert Advice 🤫
We think this is how you could land Senior Research Scientist, Intelligent Talent Acquisition - Lead Generation & Detection Services in Edinburgh
✨Tip Number 1
Network like a pro! Reach out to people in the industry, especially those at Amazon or similar companies. A friendly chat can lead to insider info and even referrals that could give you a leg up in the hiring process.
✨Tip Number 2
Prepare for your interviews by diving deep into AI and machine learning topics relevant to the role. Brush up on your knowledge of R or Python, and be ready to discuss how you've applied these skills in real-world scenarios.
✨Tip Number 3
Showcase your research experience! Be ready to talk about how you've turned complex data into actionable insights. Highlight any projects where your findings led to tangible changes in processes or outcomes.
✨Tip Number 4
Don’t forget to apply through our website! It’s the best way to ensure your application gets seen. Plus, it shows you're serious about joining the team and making an impact in talent acquisition.
We think you need these skills to ace Senior Research Scientist, Intelligent Talent Acquisition - Lead Generation & Detection Services in Edinburgh
Some tips for your application 🫡
Tailor Your CV: Make sure your CV is tailored to the role of Senior Research Scientist. Highlight your experience in quantitative research and any relevant projects that showcase your skills in AI and machine learning.
Craft a Compelling Cover Letter: Your cover letter should tell us why you're passionate about transforming the hiring landscape. Share specific examples of how your work has made an impact in previous roles, especially in talent acquisition or related fields.
Showcase Your Technical Skills: Don’t forget to mention your proficiency in R or Python, as well as any experience with AWS platforms. We want to see how you can leverage technology to solve complex business problems.
Apply Through Our Website: For the best chance of success, make sure to apply through our website. This ensures your application gets to the right people and helps us keep track of all candidates effectively.
How to prepare for a job interview at 55 Redefined Ltd
✨Know Your Research Inside Out
Make sure you’re well-versed in your own research and how it applies to the role. Be ready to discuss your PhD work or any relevant projects in detail, especially how they relate to AI and machine learning in talent acquisition.
✨Showcase Your Technical Skills
Brush up on your R or Python skills before the interview. Be prepared to discuss specific examples of how you've used these languages in your research or projects, particularly in relation to big data technologies like AWS or Hadoop.
✨Communicate Clearly with Non-Experts
Since you'll be working with cross-functional teams, practice explaining your qualitative research methods in simple terms. Think about how you can convey complex ideas clearly to those who may not have a technical background.
✨Demonstrate Your Problem-Solving Skills
Prepare to discuss real-world applications of your research. Think of examples where you've turned theoretical concepts into practical solutions, especially in the context of improving hiring processes or candidate evaluation.