Integral Ad Science is looking to improve the accuracy and increase the scope of its IVT detection capabilities in various digital platforms.
Requirements
- 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
- Expertise in standard scripting languages used in data science for statistical computation: Python, R
- Experience solving analytical problems using quantitative approaches and ML methods in a business environment
- PhD or masters in a quantitative discipline (e.g., mathematics, statistics, computer science, physics, economics, computational neuroscience)
- 4+ years experience in a business environment
Responsibilities
- Drive innovative research within the data science fraud team
- 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
- Improve the accuracy and increase the scope of IVT detection capabilities in web, mobile, CTV, gaming, social media etc.
- Collaborate with the Threat Lab to understand the nature of evolving fraud schemes
Other
- Collaborate with the Threat Lab, understand the nature of evolving fraud schemes and invent creative, quantitative solutions to identify and stop them
- Communicate the value of automated IVT detection systems to multiple stakeholders across the organization
- Socialize the predictive power and business value of data-driven ML
- Join a team of highly motivated ML researchers and developers, 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
- PhD or masters in a quantitative discipline
- 4+ years experience in a business environment
- Equal Opportunity Employer
