Applied Scientist

Join InMobi, a leading global provider of mobile advertising and marketing solutions. As an Applied Scientist III, you will work on cutting-edge research and algorithm development that drives measurable business impact. You will have the opportunity to prototype, publish, and deploy your work in a fast-paced environment that values scientific rigor and algorithmic depth.

• Formulate, analyze, and implement algorithms that power real-time auctions, dynamic pricing, bid shaping, pacing, and traffic allocation across a massive-scale ad marketplace.
• Design and experiment with methods in online learning, reinforcement learning, multi-armed bandits, forecasting, game theory, and Bayesian modeling—in non-stationary, adversarial environments.
• Collaborate with product and engineering teams to deploy your models in production and run real-world experiments with rapid feedback loops (measured in hours, not weeks).

• If you thrive on solving complex, high-impact problems and want to see your ideas shape the future of a global exchange, this is the place where your work will truly make a difference
• Deep grounding in one or more of:
• Online learning, bandits, RL, Bayesian methods
• End-to-end ownership: you can go from idea to production and thrive in applied settings
• Causal inference, decision theory, game theory
• Ph.D. (preferred) or Master’s degree in Computer Science, Statistics, Mathematics, Operations Research, Physics, or a related quantitative discipline
• Proficient in scientific computing with Python, including packages such as NumPy, SciPy, PyTorch, or TensorFlow
• 5.5–7 years of experience working on algorithmic or applied research problems, ideally with some production deployment experience
• A researcher’s mindset: questions first, implementation later. You are thoughtful about assumptions and rigorous about validation
• Prior experience in ad tech, marketplaces, or dynamic pricing is helpful but not required
• Statistical learning theory, optimization, probability theory, and information theory
• Comfortable working with big data platforms like Apache Spark, distributed computing, and large-scale datasets
• Strong publication record (e.g., NeurIPS, ICML, AISTATS, KDD, UAI, WSDM, EC, SODA, COLT) is a strong plus—even if not recent