Applied Scientist III

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).

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.

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—demonstrated research depth and production impact count.

4–7 years of experience working on algorithmic or applied research problems, including significant production deployment experience. Candidates with more or less experience are welcome to apply—we hire across Applied Scientist II, Applied Scientist III, and Staff Applied Scientist levels.

Deep grounding in one or more of: Statistical learning theory, mathematical optimization, discrete algorithms, probability theory, and information theory Causal inference, decision theory, game theory, auction theory Online learning, bandits, RL, Bayesian methods

Statistical learning theory, mathematical optimization, discrete algorithms, probability theory, and information theory

Causal inference, decision theory, game theory, auction theory

Online learning, bandits, RL, Bayesian methods

Strong publication record (e.g., NeurIPS, ICML, AISTATS, KDD, UAI, WSDM, EC, SODA, COLT) is a strong plus—even if not recent.

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. 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.

Prior experience in ad tech, marketplaces, or dynamic pricing is helpful but not required.

At InMobi, culture isn’t a buzzword; it's an ethos woven by every InMobian, reflecting our diverse backgrounds and experiences.

We thrive on challenges and seize every opportunity for growth. Our core values — thinking big, being passionate, showing accountability, and taking ownership with freedom — guide us in every decision we make.

We believe in nurturing and investing in your development through continuous learning and career progression with our InMobi Live Your Potential program.

InMobi is proud to be an Equal Employment Opportunity employer and is committed to providing reasonable accommodations to qualified individuals with disabilities throughout the hiring process and in the workplace.

Visit https://www.inmobi.com/company/careers to better understand our benefits, values, and more!