Applied Scientist III
InMobi (company site, opens in a new tab)4–7 yrsBengaluruOn-siteDirect career pageEst. 28–45 LPA
Posted on 15 Sept
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Job description
Structured overview of role & requirements
About This Role
- 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.
Minimum Requirements
- 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.
Ideal Candidate Profile
- 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.