The company is looking to improve the accuracy and increase the scope of its IVT (invalid traffic) 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 with machine learning (ML) and quantitative approaches in a business environment
- 4+ years experience solving analytical problems using quantitative approaches and ML methods
- PhD or masters in a quantitative discipline (e.g., mathematics, statistics, computer science, physics, economics, computational neuroscience)
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
- Collaborate with the Threat Lab to understand the nature of evolving fraud schemes
- Communicate the value of automated IVT detection systems to multiple stakeholders
Other
- 4+ years experience in a business environment
- PhD or masters in a quantitative discipline
- Enthusiasm for telling stories with data
- An innate curiosity about data problems
- A love of science, the scientific method, and faith in your fellow practitioners in the scientific trade
