Amazon Worldwide Advertising is one of Amazon's fastest growing and most profitable businesses.Partner Enablement and Growth (PEG) team leverages advanced analytics and experimentation to unlock value for our advertising partners and mutual customers i.
e. the advertisers. The Science charter in PEG spearheads these efforts by optimizing the onboarding journey and providing data-driven insights to agencies and tool providers in the Amazon Ads ecosystem.
Our proprietary tools provide granular visibility into what's working across audiences, creatives, targeting strategies, and more.
These custom algorithmic recommendations uncover growth opportunities previously hidden in the data fog. Partners can confidently allocate budgets based on predicted performance, unlocking improved return on ad spend.
And advertisers reap the benefits through expanded brand awareness, increased sales, and stronger customer loyalty over time.
Key job responsibilities
You will participate in driving features from idea to deployment, and your work will directly impact millions of customers.
You are going to love this job because you will :
- Map business requirements and customer needs to a scientific problem.
- Align the research direction to business requirements and make the right judgments on research / development schedule and prioritization.
- Research, design and implement scalable machine learning (ML), natural language, or computational models to solve problems that matter to our customers in an iterative fashion.
- Mentor and develop junior applied scientists and developers who work on applied science problems in the same organization.
- Stay informed on the latest machine learning, natural language and / or artificial intelligence trends and make presentations to the larger engineering and applied science communities.
BASIC QUALIFICATIONS
- 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
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.
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