Applied Scientist III

Overview

Applied Scientist III role on InMobi's algorithmic and research science team, working on research-driven problems at production scale across the InMobi Exchange, from formulating algorithms to deploying them in production.

What you'll do

• 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 models in production and run real-world experiments with rapid feedback loops (measured in hours, not weeks).
• Participate in scientific design reviews and contribute to raising the methodological bar of the team.
• Contribute to the scientific community by publishing high-quality research, conducting internal seminars, and staying abreast of advances in machine learning, algorithms, and applied statistics.
• Evaluate the long-term dynamics of deployed algorithms, incorporating feedback, exploitation-exploration trade-offs, and incentives within multi-agent systems.
• Help identify new areas for innovation by translating business challenges into research questions and proposing novel, high-impact methodologies.
• Translate mathematical ideas into practical, high-performance algorithms that operate at scale in production environments.
• Explore and close the loop between model predictions and real-world outcomes, refining algorithms based on system behavior.

What you'll need

• 4–7 years of experience working on algorithmic or applied research problems, including significant production deployment experience.
• Deep grounding in one or more of: statistical learning theory, mathematical optimization, discrete algorithms, probability theory, and information theory.
• Deep grounding in one or more of: causal inference, decision theory, game theory, auction theory.
• Deep grounding in one or more of: online learning, bandits, RL, Bayesian methods.
• 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.
• A researcher's mindset: questions first, implementation later; thoughtful about assumptions and rigorous about validation.
• End-to-end ownership: can go from idea to production and thrive in applied settings.

Nice to have

• A Ph.D. in Computer Science, Statistics, Mathematics, Operations Research, Physics, or a related quantitative discipline is strongly preferred; a degree is not a hard requirement, as demonstrated research depth and production impact count.
• Strong publication record (e.g., NeurIPS, ICML, AISTATS, KDD, UAI, WSDM, EC, SODA, COLT) is a strong plus, even if not recent.
• Prior experience in ad tech, marketplaces, or dynamic pricing is helpful but not required.

Details

• Location: Bangalore.
• Candidates with more or less experience are welcome to apply; hiring across Applied Scientist II, Applied Scientist III, and Staff Applied Scientist levels.