Integral Ad Science (IAS) is looking to solve the problem of invalid traffic (IVT) detection in the advertising industry, specifically in web, mobile, CTV, gaming, and social media, by leveraging machine learning (ML) and data science.
Requirements
- PhD or masters in a quantitative discipline (e.g., mathematics, statistics, computer science, physics, economics, computational neuroscience)
- 6-10 years experience solving analytical problems using quantitative approaches and ML methods in a business environment
- Practical experience building ML systems, ideally using weak supervision and automated data labeling.
- Knowledge of cutting edge research in ML applications and deep learning
- Capable of using SQL to answer key data questions at the drop of a hat
- Expertise in standard scripting languages used in data science for statistical computation: Python, R
- Enthusiasm for telling stories with data, deep understanding of how data works and flows through systems to produce business outcomes
Responsibilities
- Drive innovative research within the data science fraud team, improve the accuracy and increase the scope of our IVT detection capabilities in web, mobile, CTV, gaming, social media etc.
- Collaborate with the Threat Lab, understand the nature of evolving fraud schemes and invent creative, quantitative solutions to identify and stop them
- Develop automated IVT detection systems based on science, data, and ML applications.
- Collaborate with engineers to integrate fraud solutions within larger engineering workflows
- Mentor junior data scientists to innovate and build high quality fraud solutions
- Respond to internal and client-facing incidents as they arise
- Own projects from end-to-end, while collaborating with team members, learning, mentoring, contributing to the collective impact data science has on the IAS business
Other
- 6-10 years experience in a business environment
- PhD or masters degree in a quantitative discipline
- Strong commitment to getting to the bottom of things
- Love of science, the scientific method, and faith in your fellow practitioners in the scientific trade
- Ability to work in a collaborative environment
