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- Tasks: Develop machine learning algorithms and collaborate with teams to enhance Prime Video's features.
- Company: Join Amazon, a global leader in entertainment and technology, shaping the future of streaming.
- Benefits: Enjoy opportunities for growth, a dynamic work environment, and the chance to make a global impact.
- Why this job: Be part of an innovative team that values creativity and customer satisfaction in a fast-paced setting.
- Qualifications: Master's degree in a quantitative field and experience in programming and machine learning required.
- Other info: Work in a diverse and inclusive culture that prioritises employee well-being and development.
The predicted salary is between 48000 - 84000 £ per year.
Description
Come build the future of entertainment with us. Are you interested in shaping the future of movies and television? Do you want to define the next generation of how and what Amazon customers are watching?
Prime Video is a premium streaming service that offers customers a vast collection of TV shows and movies – all with the ease of finding what they love to watch in one place. We offer customers thousands of popular movies and TV shows including Amazon Originals and exclusive licensed content to exciting live sports events. We also offer our members the opportunity to subscribe to add-on channels which they can cancel at anytime and to rent or buy new release movies and TV box sets on the Prime Video Store. Prime Video is a fast-paced, growth business – available in over 200 countries and territories worldwide. The team works in a dynamic environment where innovating on behalf of our customers is at the heart of everything we do. If this sounds exciting to you, please read on.
The Insights team is looking for an Applied Scientist for our London office experienced in generative AI and large models. This is a hands-on role with Prime Video wide impact working with development teams across the UK, India, and the US. This greenfield project will deliver features that reduce the operational load for internal Prime Video builders and for this, you will need to develop personalized recommendations for their services.
You will lead the design of machine learning models that scale to very large quantities of data across multiple dimensions. You will embody scientific rigor, designing and executing experiments to demonstrate the technical effectiveness and business value of your methods. You will work alongside other scientists and engineering teams to deliver your research into production systems.
Successful candidates will have strong technical ability, excellent teamwork and communication skills, and a strong motivation to deliver customer value from their research. Our position offers exceptional opportunities for every candidate to grow their technical and non-technical skills and make a global impact immediately.
Key job responsibilities
– Develop machine learning algorithms for high-scale recommendations problems.
– Rapidly design, prototype and test many possible hypotheses in a high-ambiguity environment, making use of both quantitative analysis and business judgement.
– Collaborate with software engineers to integrate successful experimental results into Prime Video wide processes.
– Communicate results and insights to both technical and non-technical audiences, including through presentations and written reports.
About the team
Our team owns Prime Video observability features for development teams. We consume PBs of logs daily which feed into multiple observability features focussed on reducing the customer impact time. In 2025, we are expanding our remit to consume data from more sources to provide more holistic observability for our development teams.
Basic Qualifications
– Master\’s degree in engineering, technology, computer science, machine learning, robotics, operations research, statistics, mathematics or equivalent quantitative field
– Experience programming in Java, C++, Python or related language
– Experience with neural deep learning methods and machine learning
– Experience in building machine learning models for business application
– Experience in applied research
Preferred Qualifications
– Experience with modeling tools such as R, scikit-learn, Spark MLLib, MxNet, Tensorflow, numpy, scipy etc.
– Experience with large scale distributed systems such as Hadoop, Spark etc.
– PhD
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. Please consult our Privacy Notice ( ) to know more about how we collect, use and transfer the personal data of our candidates.
Amazon is an equal opportunity employer and does not discriminate on the basis of protected veteran status, disability, or other legally protected status.
Our inclusive culture empowers Amazonians to deliver the best results for our customers. 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 for more information. If the country/region youu2019re applying in isnu2019t listed, please contact your Recruiting Partner.
Senior Applied Scientist,Insights,Prime Video employer: Amazon
Contact Detail:
Amazon Recruiting Team
StudySmarter Expert Advice 🤫
We think this is how you could land Senior Applied Scientist,Insights,Prime Video
✨Tip Number 1
Familiarise yourself with the latest trends in generative AI and large models. This will not only help you understand the role better but also allow you to engage in meaningful conversations during interviews, showcasing your passion and knowledge.
✨Tip Number 2
Network with current or former employees of Prime Video, especially those in the Insights team. They can provide valuable insights into the company culture and expectations, which can be beneficial for tailoring your approach.
✨Tip Number 3
Prepare to discuss specific machine learning projects you've worked on, particularly those that involved high-scale recommendations. Be ready to explain your thought process, challenges faced, and how you overcame them.
✨Tip Number 4
Practice communicating complex technical concepts in simple terms. Since you'll need to present findings to both technical and non-technical audiences, honing this skill will make you stand out as a candidate who can bridge the gap between teams.
We think you need these skills to ace Senior Applied Scientist,Insights,Prime Video
Some tips for your application 🫡
Tailor Your CV: Make sure your CV highlights relevant experience in machine learning, programming languages like Python or Java, and any work with large-scale data systems. Emphasise your hands-on experience with generative AI and your ability to develop algorithms for high-scale recommendation problems.
Craft a Strong Cover Letter: In your cover letter, express your passion for shaping the future of entertainment through technology. Mention specific projects or experiences that demonstrate your ability to work in a dynamic environment and your motivation to deliver customer value.
Showcase Your Technical Skills: Include a section in your application that details your technical skills, particularly in neural deep learning methods and machine learning. Highlight any experience with tools like TensorFlow, scikit-learn, or large-scale distributed systems such as Hadoop.
Prepare for Communication: Since the role requires communicating results to both technical and non-technical audiences, prepare examples of how you've effectively communicated complex ideas in the past. This could be through presentations, reports, or collaborative projects.
How to prepare for a job interview at Amazon
✨Showcase Your Technical Skills
As a Senior Applied Scientist, you'll need to demonstrate your expertise in machine learning and programming. Be prepared to discuss your experience with algorithms, neural networks, and any relevant tools like TensorFlow or scikit-learn. Bring examples of past projects where you've successfully applied these skills.
✨Communicate Clearly
You'll be working with both technical and non-technical teams, so it's crucial to communicate your ideas effectively. Practice explaining complex concepts in simple terms and be ready to present your findings in a clear and engaging manner. This will show that you can bridge the gap between data science and business needs.
✨Prepare for Hypothetical Scenarios
Expect to face high-ambiguity situations during the interview. Prepare to discuss how you would approach designing and testing hypotheses in uncertain environments. Think about how you would balance quantitative analysis with business judgement to make decisions.
✨Demonstrate Team Collaboration
Collaboration is key in this role, so be ready to share examples of how you've worked effectively within a team. Highlight experiences where you've partnered with software engineers or other scientists to integrate research into production systems, showcasing your ability to work towards common goals.
