Staff Data Scientist I

**InMobi Advertising** is a global technology leader helping marketers win the moments that matter. Our advertising platform reaches over 2 billion people across 150+ countries and turns real-time context into business outcomes, delivering results grounded in privacy-first principles. Trusted by 30,000+ brands and leading publishers, InMobi is where intelligence, creativity, and accountability converge. By combining lock screens, apps, TVs, and the open web with AI and machine learning, we deliver receptive attention, precise personalization, and measurable impact.

Through **Glance AI,** we are shaping AI Commerce, reimagining the future of e-commerce with inspiration-led discovery and shopping. Designed to seamlessly integrate into everyday consumer technology, Glance AI transforms every screen into a gateway for instant, personal, and joyful discovery. Spanning diverse categories such as fashion, beauty, travel, accessories, home décor, pets, and beyond, Glance AI delivers deeply personalized shopping experiences. With rich first-party data and unparalleled consumer access, it harnesses InMobi's global scale, insights, and targeting capabilities to create high impact, performance driven shopping journeys for brands worldwide.

Recognized as a Great Place to Work, and by MIT Technology Review, Fast Company's Top 10 Innovators, and more, InMobi is a workplace where bold ideas create global impact. Backed by investors including SoftBank, Kleiner Perkins, and Sherpalo Ventures, InMobi has offices across San Mateo, New York, London, Singapore, Tokyo, Seoul, Jakarta, Bengaluru and beyond.

**Overview of the role**:
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 a Staff 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—while providing thought leadership to the broader team. 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.

**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).
- **Provide technical leadership by reviewing scientific approaches, mentoring junior scientists, and guiding the team toward robust methodologies.**
- 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.
- Explore and close the loop between model predictions and real-world outcomes, refining algorithms based on system behavior.

**The experience we need**:

- Ph.D. (preferred) or Master's degree in Computer Science, Statistics, Mathematics, Operations Research, Physics, or a related quantitative discipline.
- **7-10 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
- 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.
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