Data Scientist, Prime Video Content Analytics

☞ Amazon Data Services UK Ltd

View: 103

Update day: 01-06-2024

Location: London

Category: Marketing / PR Science Labor Pharmaceutical / Chemical / Biotech

Industry:

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Job content

  • Experience working as a Data Scientist
  • Experience with data scripting languages (e.g. SQL, Python, R etc.) or statistical/mathematical software (e.g. R, SAS, or Matlab)
  • Previous experience with reporting and visualization using R/Shiny, Tableau or other similar data visualization and reporting packages
  • Expertise in predictive modelling, including both parametric (e.g. logit/probit) and non-parametric (e.g. random forest, neural net) techniques
  • Proven ability to balance competing priorities, workloads, and tight timelines in a fast-paced, dynamic work environment
  • Excellent verbal/written communication & data presentation skills, including an ability to effectively communicate with both business and technical teams
Job summary
Amazon is seeking an outstanding Data Scientist to uncover key insights on how customers engage with live sports events on Prime Video globally. With prestigious sporting events on Prime Video including the English Premiere League (UK), UEFA Champions League (Italy, Germany), Ligue 1 (France), US Open Tennis (UK), Roland Garros (France), Autumn Nations Cup Rugby (UK) and more, live sports are an integral and growing component of Prime Video. As our selection of events expands, the Prime Video Content Analytics team are looking to enable agile decision making on live sports by developing key insights into customer engagement with live sport and translating these insights into large scale predictive modelling and analytics solutions.

You will have the following responsibilities within the scope of our global Prime Video business:
  • Drive analytics in an uncharted field that is developing at a fast pace but also becoming increasingly important to Prime Video
  • Support the analytical needs of stakeholders in the sports, advertising, finance and live events teams, inclusive of statistical inferences, demand modelling, feature engineering
  • Build profitability models for new sports rights and partner with finance on business cases
  • Think outside the box to use novel data and methodological approaches
  • Create new metrics that effectively guide the business and deploy dashboards to surface them to senior leadership
  • Ensure that the quality and timeliness of analytic deliverables meet business expectations
  • 2+ years experience working as a Data Scientist
  • Master’s degree in machine learning, operational research, computer science, statistics, applied mathematics or a related field; or equivalent experience
  • Proven ability to work effectively in a cross-functional, fast paced environment
  • Proven ability to understand and communicate issues at multiple levels, from validating and correcting junior staff detailed work to explaining the implications of analysis results to executives
  • Experience working in the media or live sports industries
Amazon Science (www.amazon.science) gives you insight into the company’s approach to customer-obsessed scientific innovation. Amazon fundamentally believes that scientific innovation is essential to being the most customer-centric company in the world. It’s the company’s ability to have an impact at scale that allows us to attract some of the brightest minds in artificial intelligence and related fields. Our scientists continue to publish, teach, and engage with the academic community, in addition to utilizing our working backwards method to enrich the way we live and work.

#scienceemea

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 (https://www.amazon.jobs/en/privacy_page) to know more about how we collect, use and transfer the personal data of our candidates.
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Deadline: 16-07-2024

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