Applied Scientist III

What you'll do

• Formulate, analyze, and implement algorithms for real-time auctions, dynamic pricing, bid shaping, pacing, and traffic allocation
• Design and experiment with online learning, reinforcement learning, multi-armed bandits, forecasting, game theory, and Bayesian modeling methods
• Collaborate with product and engineering teams to deploy models in production and run real-world experiments with rapid feedback loops
• Publish high-quality research and conduct internal seminars
• Evaluate long-term dynamics of deployed algorithms incorporating feedback and incentives within multi-agent systems
• Identify new areas for innovation by translating business challenges into research questions
• Translate mathematical ideas into practical, high-performance algorithms at scale
• Refine algorithms based on system behavior and real-world outcomes

Requirements

• Ph.D. (preferred) or Master’s degree in Computer Science, Statistics, Mathematics, Operations Research, Physics, or a related quantitative discipline
• 5.5–7 years of experience working on algorithmic or applied research problems, ideally with some production deployment experience
• Deep grounding in statistical learning theory, optimization, probability theory, information theory, causal inference, decision theory, game theory, online learning, bandits, RL, Bayesian methods
• Strong publication record (e.g., NeurIPS, ICML, AISTATS, KDD, UAI, WSDM, EC, SODA, COLT) is a strong plus
• Proficient in scientific computing with Python, including packages such as NumPy, SciPy, PyTorch, or TensorFlow
• Comfortable working with big data platforms like Apache Spark, distributed computing, and large-scale datasets
• Researcher’s mindset: thoughtful about assumptions and rigorous about validation
• End-to-end ownership: ability to go from idea to production
• Prior experience in ad tech, marketplaces, or dynamic pricing is helpful but not required

Tech stack

Benefits