Overview
There are trillions of events a day in our system, requiring models to run at a tremendous scale with millisecond latency. Our learning loop is measured in hours and minutes, making it one of the fastest model-learning playgrounds in the world. We have built an infrastructure that enables model deployment at speed, with data scientists working alongside engineering colleagues. This, combined with a growing set of hundreds of potential features, creates a highly fertile environment for building, experimenting, refining, and achieving real impact from models. Immediate bottom-line impact is seen when models are effective.
This position is located onsite in San Mateo, CA and is not open to remote locations.
The Impact You'll Make
- Lead data science efforts for one of the biggest in-app programmatic exchanges globally, including project ideation, conceptualization, solution design, measurement, iteration, coaching, deployment, and post-deployment management.
- Design, develop, and test product experiments. Guide the team in practical experiments, product design, model development, and model evaluation, focusing on agility and rapid iteration to deliver go-to-market ready products.
- Actively analyze data, design and develop models, and problem-solve solutions as a hands-on team member.
- Manage stakeholders, serving as the interface with internal teams such as Product, Engineering, Data, Infrastructure, and Business.
- Contribute to thought leadership by writing blogs, commentary, and case studies for the InMobi blog, and speaking at industry conferences to represent InMobi’s work.
- Design and build models for specific business problems, identifying areas where AI can be applied for best business impact, anchoring in business context and end-user needs, and connecting model impact with measurable business outcomes.
- Collaborate within a multi-functional team environment, leveraging skills from diverse individuals across engineering, product, business, campaign management, and creative development.
- Experiment with multiple algorithms, gaining enduring learning from building, launching, reviewing performance, and tailoring techniques to fit problems at hand in a fast-paced environment.
- Develop creative approaches to designing successful models, recognizing that model design is not one-size-fits-all and often requires layers of models and feedback mechanisms in dynamic environments.
- Innovate and demonstrate thought leadership through products, research papers, or conferences, with ample opportunities to shine.
The Experience You'll Need
- A strong foundation in Mathematics, Statistics, Algorithms, Optimization, and competent ability in coding with data science languages and tools such as Python or Apache Spark.
- A passion for investigating and learning from data, asking interesting questions, and being driven to put real models into production that drive business value.
- Understanding of big data processing and cloud computing fundamentals.
- Openness to diverse academic backgrounds, demonstrating intent to think and problem-solve like a data scientist.
Requirements
- Master’s degree in a quantitative field such as Computer Science, Statistics, Electrical Engineering, Mathematics, Operations Research, Economics, Analytics, or Data Science. Ph.D. is a significant plus.
- Depending on the level, experience in the Ad Tech Industry working in Data Science teams, applying algorithms and techniques from Machine Learning, Statistics, Time Series, or other domains to solve real-world problems on large datasets.
- Passion for Mathematics, Algorithms, and Machine Learning, eager to learn and apply cutting-edge science to InMobi business problems, and excited by the real-world impact of models in production with fast execution.
- Intellectual depth to translate ambiguous business problems into rigorous mathematical problem statements and algorithms.
- Experience and passion in troubleshooting when ML models do not produce production lift.
- Comfortable with software programming and statistical platforms such as R and Python, and comfortable with the big data ecosystem, including experience in Apache Spark.