Data Scientist, Traffic Quality

Key Skills

Amazon Elastic Compute Cloud-EC2, Amazon EMR, Amazon RedShift, Amazon Simple Storage Service (S3), Apache Hadoop, HDFS, Java Programming, MATLAB Programming, Python Programming, R Programming, SAS, SPARK Programming, SQL

Job Description

Advertising at Amazon is a fast-growing, multi-billion dollar business. It spans desktop, mobile and connected devices; encompasses ads on Amazon and a vast network of hundreds of thousands of third-party publishers; and extends across the US, EU, APAC, and an increasing number of international geographies.

The Supply Quality (SQ) group operates across all Amazon Advertising products to provide invalid traffic (IVT) filtration, viewability prediction and measurement, and content adjacency policy enforcement capabilities.

Within Supply Quality, the Traffic Quality (TQ) for Amazon DSP program leverages proprietary and third-party technology to provide invalid traffic (IVT) detection and filtration capabilities to Amazon DSP customers.

The TQ-DSP program is looking for a Data Scientist to support its science team who will work closely with business stakeholders and leaders across Amazon Advertising to build cutting edge IVT detection models while preserving end user privacy. you will work with the team to provide predictive IVT research to TQ-DSP, including actionable insights to mitigate specific ad fraud methods. You are an experienced user of sophisticated analytical techniques that can be applied to answer business questions and chart a sustainable vision. You are able to build on the analysis to communicate insights and recommendations to audiences of varying levels of technical sophistication. Above all, you are an innovator at heart and have a track record of resolving ambiguity to deliver results. You also possess excellent interpersonal skills, strong written communication skills, are able to develop and coach people, and oversee a portfolio of key business initiatives.

Key job responsibilities

• Define a long-term science vision for TQ-DSP, driven fundamentally from the needs of our advertisers and publishers, translating that direction into specific plans for the science team. Interpret complex and interrelated data points and anecdotes to build and communicate this vision.
• Identify and implement elegant statistical and machine learning solutions to detect robotic and human traffic.
• Oversee the design, development, and implementation of production level code that handles billions of ad requests. Own the full development cycle: idea, design, prototype, impact assessment, A/B testing (including interpretation of results) and production deployment.
• Collaborate with engineers, product managers and related teams in Amazon Advertising to find technical solutions to complex IVT detection problems.
• Improve upon existing methodologies for supporting new data sources, implementing model enhancements, and fine-tuning model parameters.
• Influence and continuously improve a sustainable team culture that exemplifies Amazon’s leadership principles.

BASIC QUALIFICATIONS

• Bachelor's degree + 2 or Master's degree in a quantitative field.
• 1+ 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.
• 2+ years of experience with data scripting languages (e.g SQL, Python, R etc.) or statistical/mathematical software (e.g. R, SAS, or Matlab)
• Clear oral and written communication skills. Demonstrated evidence of building lasting relationships with the team and business stakeholders.

PREFERRED QUALIFICATIONS

• 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.