InMobi Advertising is looking to solve complex, high-impact problems at production scale within their algorithmic and research science team. This involves designing and implementing algorithms for traffic shaping, fraud detection, ad quality, pricing strategies, and auction theory to optimize core business functions and create a strategic market advantage.
Requirements
- Deep grounding in one or more of: Statistical learning theory, optimization, probability theory, and information theory
- Deep grounding in one or more of: Causal inference, decision theory, game 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.
- Prior experience in ad tech, marketplaces, or dynamic pricing is helpful but not required.
Responsibilities
- 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).
- 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.
- 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.
Other
- 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.
- Strong publication record (e.g., NeurIPS, ICML, AISTATS, KDD, UAI, WSDM, EC, SODA, COLT) is a strong plus—even if not recent.
- A researcher’s mindset: questions first, implementation later. You are thoughtful about assumptions and rigorous about validation.
- End-to-end ownership: you can go from idea to production and thrive in applied settings.
