At OpenX, we have built a team that is uniquely experienced in designing and operating high-scale ad marketplaces, and we are constantly on the lookout for thoughtful, creative executors who are as fascinated as we are about finding new ways to apply a blend of market design, technical innovation, operational excellence, and empathetic partner service to the frontiers of digital advertising.
A Data Scientist III is a proficient, fully independent scientist who owns medium-to-large data science projects end-to-end from problem formulation and research through to deploying and maintaining production models. In this role, you will build production-ready models and analyses that solve real marketplace problems, partner with product and engineering to ship them, mentor junior scientists, and act as a strong technical voice within your team.
Problems at this level include bidding and yield modeling, relevance and prediction systems at exchange scale, experimentation and causal measurement of marketplace changes, and the feature engineering, validation, and monitoring required to run ML reliably in production.
The ideal candidate brings a solid applied machine learning foundation, growing judgment in selecting methods for business problems at scale, and a track record of carrying analytical work from an ambiguous question through to measurable production impact.
- B.S. or M.S. in Data Science, Machine Learning, Computer Science, Physics, Mathematics, Operations Research, or a related technical field with 5+ years of relevant industry experience; OR a Ph.D. in a related field with 2+ years of relevant experience.
- Demonstrated ability to independently own the full data science lifecycle from problem formulation and feature engineering through model deployment, monitoring, and ongoing maintenance.
- Solid expertise in several core areas of machine learning and/or statistics (e.g., gradient-boosted models, deep neural networks, time series, causal inference, experimentation design), with the judgment to select appropriate methods for complex problems.
- Strong foundation in probability and statistics, including techniques that scale to large datasets.
- Experience designing and analyzing experiments (e.g., A/B testing) and building robust model and experiment validation frameworks.
- Strong Python and SQL skills; experience with ML frameworks such as TensorFlow or PyTorch.
- Ability to write efficient, modular, well-tested code and to collaborate with engineering to move models and analyses into production.
- Strong communication skills, including the ability to convey complex technical concepts to both technical and non-technical audiences.