Applied Science Manager
Bangalore, IN
1d ago

Amazon is investing heavily in building a world class advertising business and we are responsible for defining and delivering a collection of self-service performance advertising products that drive discovery and sales.

Our products are strategically important to our Retail and Marketplace businesses driving long term growth. We deliver billions of ad impressions and millions of clicks daily and are breaking fresh ground to create world-class products.

The Moderation and Relevance System (MARS) team, based in Bangalore, is responsible for ensuring that ads are compliant to world-wide advertising policies and are of high quality, leading to higher conversion for the advertisers and providing a great experience for the shoppers.

Machine learning is fundamental to the way we drive our business, meet our goals and satisfy our customers. MARS team invests in researching and developing state of art models that analyze various type of ad assets text, audio, images and videos - to ensure compliance to advertising policies.

It also helps advertisers create more successful ads by creating ML models to generate content and signals for various upstream and downstream pipelines in the ad journey.

Major responsibilities

  • Deliver key goals to enhance advertiser experience and protect shopper trust by innovative use of machine learning and statistical techniques
  • Drive core business analytics and data science explorations to inform key business decisions and algorithm roadmap
  • Establish scalable, efficient, automated processes for large scale data analyses, model development, model validation and model implementation
  • Hire and develop top talent in machine learning and data science and accelerate the pace of innovation in the group
  • Build a culture of innovation and long-term thinking, and showcase this via peer-reviewed publications and whitepapers
  • Work proactively with engineering teams and product managers to evangelize new algorithms and drive the implementation of large-scale complex ML models in production
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