Applied Scientist III

Skills this role screens for

About the role

InMobi (Corporate)

InMobi Group is a global technology company shaping the future of agentic commerce and advertising. Through its ecosystem of businesses — including InMobi Advertising and flagship consumer platform Glance — InMobi leverages data, machine learning, and generative AI to help brands reach audiences more precisely and consumers discover products more intuitively. Glance, which is pioneering new models of agentic commerce, is owned and operated by Glance InMobi Pte. Ltd., a non-consolidated subsidiary of InMobi Pte. Ltd.

InMobi Advertising

InMobi Advertising, part of global technology company InMobi, is an agentic advertising platform helping brands and merchants achieve their business outcomes. Through its proprietary intelligence, AI-led solutions, and vast consumer reach — including flagship consumer platform Glance — InMobi Advertising delivers the omnichannel performance defining what's next in advertising and commerce. Glance is owned and operated by Glance InMobi Pte. Ltd., a non-consolidated subsidiary of InMobi Pte. Ltd. To learn more, visit advertising.inmobi.com.

Glance

Glance is an intelligent shopping agent, redefining the commerce experience. Powered by proprietary agentic intelligence and generative AI, Glance delivers a hyper-personalized consumer experience across mobile and TV — shaping the new era of shopping. Glance is operated by Glance InMobi Pte. Ltd., a non-consolidated subsidiary of global technology leader InMobi, and is backed by Mithril Capital, Google and Jio Platforms. To learn more, visit glance.com.

The work

Overview of the role

We are looking for an Applied Scientist III to join our algorithmic and research science team. You'll work on mathematically rigorous, research-driven problems at production scale, while owning problems end to end. This role sits at the intersection of theory and application, designing algorithms that combine elegant modeling with measurable business impact. Specifically, our scientists tackle challenges across traffic shaping, fraud detection, ad quality, pricing strategies, and auction theory, along with their practical applications. We leverage the latest deep learning models alongside classical machine learning techniques to build innovative solutions.

As the heart of the InMobi Exchange, our team optimizes the company's core business functions and creates the strategic moat that sets us apart in the market. As an Applied Scientist, you will not just "use models"—you will formulate them, evaluate their assumptions, tailor them to our problem domain, and bring them to life in production. Many of our challenges have no off-the-shelf solutions; we require scientific creativity to bridge research and reality.

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.

Also

The impact you'll make

• 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.

Who they want

The experience we need

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

The InMobi Culture

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!

• 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
• 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.