The company 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 with machine learning and data science
- Experience with data analysis and statistical modeling
- Experience with data visualization and communication
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
- Improve the accuracy and increase the scope of IVT detection capabilities in web, mobile, CTV, gaming, social media etc.
Other
- PhD or masters in a quantitative discipline
- 6-10 years experience solving analytical problems using quantitative approaches and ML methods in a business environment
- Enthusiasm for telling stories with data, deep understanding of how data works and flows through systems to produce business outcomes
- An innate curiosity about data problems, strongly held commitment to getting to the bottom of things
- A love of science, the scientific method, and faith in your fellow practitioners in the scientific trade
