Applied Scientist, Stores Ads Science

Amazon • Seattle, WA

1d

About The Position

Amazon Stores Ads Science team is looking for a Senior Applied Scientist to help
translate state-of-the-art causal inference and machine learning research into
production solutions. The individual will have the opportunity to shape the
technical and strategic vision of a highly ambiguous problem space at the
intersection of Amazon’s Stores and Advertising businesses, and deliver
measurable business impacts via cross-team and cross-functional collaboration.

Amazon is investing heavily in building a world-class advertising business. Our
advertising products are strategically important to Amazon’s Retail and
Marketplace businesses for driving the long-term growth. The mission of the
Stores Ads Science team is to identify opportunities to jointly optimize
Amazon’s Stores and Advertising business, by enhancing the usage of advertising
signals in Stores’ decision-making and vice versa to drive the long-term
economic value to shoppers, sellers/vendors, and Amazon. Some of our work
include measuring ads impact and shopping content incrementality to improve
shopper experience, developing the science of explaining seller/vendor
inter-related decisions between Stores and Ads (e.g.: pricing, Sponsored Ads
participation) to optimize fees and incentives, and making Stores’ traffic
acquisition strategies aware of shoppers’ and advertisers’ onsite advertising
behavior.

We partner closely with tech and product teams across Stores and Advertising,
and are constantly advancing experimentation methodology to accelerate science
development and quantify business impacts. We are highly motivated,
collaborative, and fun-loving with an entrepreneurial spirit and bias for
action.

Requirements

• 3+ years of building machine learning models for business application experience
• PhD, or Master's degree and 6+ years of applied research experience
• Experience programming in Java, C++, Python or related language
• Experience with neural deep learning methods and machine learning

Nice To Haves

• 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.

Benefits

• health insurance (medical, dental, vision, prescription, Basic Life & AD&D insurance and option for Supplemental life plans, EAP, Mental Health Support, Medical Advice Line, Flexible Spending Accounts, Adoption and Surrogacy Reimbursement coverage)
• 401(k) matching
• paid time off
• parental leave

What This Job Offers

• Job Type: Full-time
• Career Level: Mid Level
• Education Level: Ph.D. or professional degree