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- Tasks: Develop predictive models and insights to shape Prime Video's content strategy.
- Company: Join Amazon's innovative team at Prime Video, shaping the future of entertainment.
- Benefits: Competitive salary, diverse work culture, and opportunities for professional growth.
- Why this job: Make a real impact on how millions enjoy movies and TV shows worldwide.
- Qualifications: Experience in data science, machine learning, and scripting languages like Python or SQL.
- Other info: Dynamic environment with a focus on innovation and customer satisfaction.
The predicted salary is between 36000 - 60000 £ per year.
In this role you will work closely with business stakeholders and other data scientists to develop predictive models, forecast key business metrics, dive deep on the customer and content related factors that drive engagement and create mechanisms and infrastructure to deploy complex models and generate insights at scale. You will have the opportunity to work with large datasets, build with AWS to deploy machine learning and forecasting models while making a significant impact on how Prime Video makes content investment and selection decisions.
Experience:
- Machine learning/statistical modeling data analysis tools and techniques, and parameters that affect their performance
- Applying theoretical models in an applied environment
- Working as a Data Scientist
- Data scripting languages (e.g. SQL, Python, R etc.) or statistical/mathematical software (e.g. R, SAS, or Matlab)
Preferred Qualifications:
- Experience in Python, Perl, or another scripting language
- Experience in a ML or data scientist role with a large technology company
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.
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.
Data Scientist, Prime Video Forecasting Science in City of Westminster employer: Amazon.com, Inc
Contact Detail:
Amazon.com, Inc Recruiting Team
StudySmarter Expert Advice 🤫
We think this is how you could land Data Scientist, Prime Video Forecasting Science in City of Westminster
✨Tip Number 1
Network like a pro! Reach out to current or former employees at Prime Video on LinkedIn. A friendly chat can give you insider info and maybe even a referral, which can really boost your chances.
✨Tip Number 2
Show off your skills! Prepare a portfolio of your data science projects, especially those involving predictive models or large datasets. When you get the chance to chat with hiring managers, having tangible examples will make you stand out.
✨Tip Number 3
Practice makes perfect! Brush up on your technical skills, especially in Python and SQL. You might face some coding challenges during interviews, so being well-prepared can help you ace them.
✨Tip Number 4
Apply through our website! It’s the best way to ensure your application gets seen. Plus, it shows you’re genuinely interested in joining the Prime Video team and shaping the future of entertainment with us.
We think you need these skills to ace Data Scientist, Prime Video Forecasting Science in City of Westminster
Some tips for your application 🫡
Tailor Your CV: Make sure your CV highlights your experience with predictive models and data analysis tools. We want to see how your skills align with the role, so don’t be shy about showcasing your work with Python, SQL, or any other relevant tech!
Craft a Compelling Cover Letter: Your cover letter is your chance to shine! Use it to explain why you’re excited about the Data Scientist role at Prime Video. Share specific examples of how you've used data to drive decisions and engage customers.
Showcase Your Projects: If you’ve worked on any cool projects involving machine learning or large datasets, make sure to mention them! We love seeing real-world applications of your skills, so include links or descriptions of your work.
Apply Through Our Website: Don’t forget to apply through our website! It’s the best way for us to receive your application and ensure it gets the attention it deserves. Plus, it’s super easy to do!
How to prepare for a job interview at Amazon.com, Inc
✨Know Your Models
Make sure you can explain the predictive models you've worked with in detail. Be ready to discuss how you applied theoretical models in real-world scenarios, especially in relation to customer engagement and content selection.
✨Brush Up on Your Coding Skills
Since you'll be working with data scripting languages like SQL and Python, it’s crucial to demonstrate your proficiency. Prepare to solve coding challenges or answer technical questions that showcase your ability to manipulate large datasets.
✨Understand the Business Impact
Familiarise yourself with how data science influences business decisions, particularly in a streaming context. Be prepared to discuss how your insights could drive content investment strategies and improve customer experience on Prime Video.
✨Showcase Your Collaboration Skills
This role involves working closely with stakeholders and other data scientists. Be ready to share examples of past collaborations, highlighting how you communicated complex data insights to non-technical team members and contributed to team success.
