Applied Scientist III

About the role

Join the algorithmic and research science team to tackle mathematically rigorous problems at production scale. You will design and deploy algorithms that blend theoretical modeling with real business outcomes. The team works on traffic shaping, fraud detection, ad quality, pricing strategies, and auction theory, using both deep learning and classical machine learning approaches. This position sits at the core of the InMobi Exchange, optimizing critical business functions.

Key facts

What you'll do

• Develop and implement algorithms for real-time auctions, dynamic pricing, bid shaping, pacing, and traffic allocation across a large-scale ad marketplace handling tens of trillions of daily decisions
• Experiment with online learning, reinforcement learning, multi-armed bandits, forecasting, game theory, and Bayesian modeling in non-stationary and adversarial settings
• Partner with product and engineering teams to bring models into production and run experiments with feedback loops measured in hours
• Publish research, lead internal seminars, and stay current with advances in machine learning, algorithms, and applied statistics
• Assess long-term dynamics of deployed algorithms, including exploration-exploitation trade-offs and incentives in multi-agent systems
• Convert business challenges into research questions and propose new methodologies
• Translate mathematical concepts into high-performance, production-ready algorithms
• Refine models by closing the loop between predictions and real-world system behavior

Requirements

• Ph.D. (preferred) or Master's degree in Computer Science, Statistics, Mathematics, Operations Research, Physics, or a related quantitative field
• 5.5 to 7 years of experience in algorithmic or applied research, ideally including production deployment
• Deep expertise in at least one area: statistical learning theory, optimization, probability theory, information theory, causal inference, decision theory, game theory, online learning, bandits, reinforcement learning, or Bayesian methods
• Proficiency in scientific computing with Python (NumPy, SciPy, PyTorch, or TensorFlow)
• Experience with big data platforms such as Apache Spark, distributed computing, and large-scale datasets
• Research-oriented mindset that prioritizes questions before implementation and validates assumptions rigorously
• Ability to own projects end-to-end, from concept through production

Nice to have

• Strong publication record at venues such as NeurIPS, ICML, AISTATS, KDD, UAI, WSDM, EC, SODA, or COLT (even if not recent)
• Prior experience in ad tech, marketplaces, or dynamic pricing

Skills & tools

• Python, NumPy, SciPy, PyTorch, TensorFlow
• Apache Spark, distributed computing
• Online learning, reinforcement learning, multi-armed bandits
• Bayesian modeling, game theory, auction theory
• Forecasting, causal inference, optimization

Practical notes

• Salary range applies to California and New York offices; compensation may differ by region
• Equity participation through Restricted Stock Units may be available
• InMobi is recognized as a Great Place to Work