Data Scientist

At OpenX, the team specializes in designing and operating high-scale ad marketplaces, applying market design, technical innovation, operational excellence, and partner services in digital advertising.

• 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 Ph.D. with 2+ years relevant experience.
• Demonstrated ability to independently own the full data science lifecycle from problem formulation, feature engineering through model deployment, monitoring, and ongoing maintenance.
• Solid expertise in several core areas of machine learning and/or statistics such as gradient-boosted models, deep neural networks, time series, causal inference, and experimentation design.
• Strong foundation in probability and statistics, including techniques that scale to large datasets.
• Experience designing and analyzing experiments including 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 collaborate with engineering to move models and analyses into production.
• Strong communication skills to convey complex technical concepts to both technical and non-technical audiences.

Nice to have:

• Experience with Google Cloud Platform and AWS.

• Own the end-to-end data science lifecycle for moderately complex models and significant project components spanning data ingestion, feature engineering, modeling, validation, deployment, monitoring, and retraining.
• Apply expertise across several core areas of machine learning and statistics (e.g., gradient-boosted models, deep neural networks, time series, causal inference concepts, experimentation design), selecting appropriate methods for complex data science problems.
• Write efficient, modular, well-tested code for data processing, feature engineering, and model training/inference, leveraging distributed tooling (e.g., Vertex AI pipelines, Dataflow, BigQuery) where appropriate.
• Design and implement robust validation frameworks for complex experiments and models, accounting for potential biases and real-world performance.
• Troubleshoot complex model performance issues, data anomalies, and code bugs effectively with little guidance.
• Define analytical approaches and scope data science projects for moderately complex or ambiguous business problems.
• Partner with product managers and stakeholders to define success metrics and experiment goals, translating marketplace problems into data science solutions.
• Lead the design and analysis of experiments (e.g., A/B tests, switchback) and interpret complex model results and experimental outcomes focusing on actionable insights and business outcomes.
• Proactively identify opportunities within your domain where data science can provide significant value and initiate exploration.
• Follow and help improve established team processes for coding standards, documentation, reproducibility, and experimentation.
• Mentor, review code, analyses, and models, and influence technical decisions regarding modeling choices and validation strategies.

Tools used include Jira, Confluence, Wiki, GitHub, GIT, Agile, and Scrum.