Advertising at Amazon is a fast-growing multi-billion dollar business that spans across desktop, mobile and connected devices;
encompasses ads on Amazon and a vast network of hundreds of thousands of third party publishers; and extends across US, EU and an increasing number of international geographies.
The charter of the Traffic Quality team is to identify non-human and invalid traffic within programmatic ad sources, and weed them out to ensure a high quality advertising marketplace.
We do this by advanced analytics and machine learning algorithms that operate at scale, and leverage nuanced features about user, context, and creative engagement to determine the validity of traffic.
The challenge is to stay one step ahead by investing in deep analytics and developing new algorithms that address emergent attack vectors in a structured and scalable fashion.
We are committed to building industry-leading state-of-the-art traffic filtering for all 1P supply sources (Amazon, Twitch, IMDB) that preserves advertiser trust and saves hundreds of millions of dollars of wasted spend.
We are looking for a dynamic, innovative and accomplished data sciences manager to lead analytics and data science initiatives for the Advertising Traffic Quality vertical.
Are you excited by the prospect of analyzing terabytes of data and leveraging statistical and data science techniques to understand and solve real world problems?
Do you like to own end-to-end business problems / metrics and directly impact the profitability of the company? As a data sciences manager for Traffic Quality, you will lead a team of data scientists and analysts towards understanding advertiser experience, analyzing emergent robotic behavior and gleaning invaluable insights to guide critical algorithm investment.
Your team will run experiments with current algorithms to improve coverage and enhance precision. Your team will include data scientists and analysts, who have a keen understanding of the fraud landscape, and you will collaborate with senior applied scientists to shape the overall data science strategy for the team.
You will collaborate with engineering leaders, product managers and operations teams to deliver critical projects that cut across organization structures and meet key business goals.
Key job responsibilities
Deliver key goals to enhance advertiser experience and deliver multi-million dollar savings by building algorithms to detect and mitigate invalid traffic
Use data science and statistical techniques to create new, scalable solutions for invalid traffic filtering
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 analytics 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
Stay updated on the industry landscape in Traffic Quality and identify algorithm investments to achieve an industry leading traffic quality solution.
PhD or equivalent Master's degree plus 5+ years of research experience in a quantitative field.
3+ years experience in the application of scientific principals and concepts to business problems and products.
Demonstrated breadth and depth in quantitative modeling and statistical analysis.
5+ years of experience with data scripting languages (e.g SQL, Python, R etc.) or statistical / mathematical software (e.g. R, SAS, or Matlab)
2+ years of experience working as a Data Scientist Manager, and coaching and growing a team.
Clear oral and written communication skills. Demonstrated evidence of building lasting relationships with the team and business stakeholders.
Prior experience with cloud computing (EMR, S3, EC2, Redshift) and big data tools (Spark, Hadoop, HDFS).
Comfortable with open-ended problems. Ability to frame your own hypothesis, create meaningful metrics, and select the most reasonable modeling approach.
Demonstrated track record of dealing well with ambiguity, prioritizing needs, and delivering results in a dynamic environment.
Experience with managing a portfolio of projects and building a team of senior scientists with advanced degrees.