Back
Login to See Your Match Score
Create a free account or log in to unlock your CV match score across:
Strong employer brand, metro location, and mid-level experience increase applicant density, but specialized research requirements narrow the pool.
Role requires specialized ML research, auction theory, and ad-tech experience, making cross-industry transferability low.
Explicit 4–7 years, PhD preference, publication and production deployment requirements make screening highly selective.
Design and implement algorithms for real-time auctions, dynamic pricing, bid shaping, pacing, and traffic allocation in a large-scale ad marketplace.
Develop, experiment with, and deploy models using online learning, reinforcement learning, multi-armed bandits, game theory, and Bayesian methods in non-stationary, adversarial environments.
Own end-to-end production deployment of algorithms, collaborate with product and engineering teams, conduct scientific design reviews, and contribute to team methodological advancement.
Ph.D. in Computer Science, Statistics, Mathematics, Operations Research, Physics, or related quantitative discipline preferred but not mandatory if demonstrated research depth and production impact exist.
4–7 years experience in algorithmic or applied research problems including production deployment. Candidates with experience outside this range may be considered for level adjustment.
Strong expertise in statistical learning theory, mathematical optimization, discrete algorithms, probability, information theory, causal inference, decision theory, game theory, auction theory, online learning, bandits, reinforcement learning, or Bayesian methods.
Proficient in Python scientific computing (NumPy, SciPy, PyTorch, TensorFlow) and comfortable with big data platforms like Apache Spark and distributed computing.
Experienced researcher with ability to translate complex mathematical models into scalable, production-ready algorithms with measurable business impact.
Comfortable working at the intersection of theoretical research and applied machine learning in fast-paced environments with rapid experimental feedback.
Background or strong interest in advertising technology, marketplaces, or dynamic pricing domains with publication record in top-tier ML/statistics conferences is a plus.