Staff Applied Scientist (Ads Personalization)

Join our team as a Staff Applied Scientist specializing in Ads Personalization. In this role, you will design, implement, and maintain user understanding models, ensuring model quality and reliability through continuous monitoring and calibration. You will actively participate in setting the long-term direction for the team and effectively communicate complex technical concepts to non-engineering stakeholders.

We offer a comprehensive benefits package, including health insurance, employee stock ownership, generous vacation and personal days, office food and snacks, training and development programs, and more.

To be successful in this role, you should have 3+ years of experience in content understanding, behavior modeling, user profiling, and cohort analysis, as well as significant experience in programming languages like Python, Go, Scala, or C++. An advanced degree in Computer Science, Machine Learning, Statistics, or a related field is also required.

• Design, implement, and maintain user understanding models to enhance ad personalization.
• Monitor model performance, calibrate predictions, and address data drift to ensure quality and reliability.
• Collaborate with other leads to set the long-term direction for the team and oversee project execution.

• 3+ years of experience in content understanding, behavior modeling, user profiling, and cohort analysis
• Significant experience in one or more general-purpose programming languages like Python, Go, Scala, C++ or similar
• Advanced degree (MS or Ph.D.) in Computer Science, Machine Learning, Statistics, related field, or equivalent practical experience
• 5+ years of hands-on experience building and operating large-scale Ads delivery and optimization systems
• Knowledge of metric design, experimentation methodologies, and large-scale data analysis
• Experience designing and productionizing optimization algorithms in an ad serving platform and/or other marketplaces
• Experience with state-of-the-art control systems, reinforcement learning algorithms