Data Scientist III

OpenX is focused on unleashing the full economic potential of digital media companies by making digital advertising markets and technologies designed to deliver optimal value to publishers and advertisers on every ad served across all screens. We have built a team uniquely experienced in designing and operating high-scale ad marketplaces and are constantly seeking thoughtful, creative executors fascinated by applying a blend of market design, technical innovation, operational excellence, and empathetic partner service to the frontiers of digital advertising.

About the role

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 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.

Responsibilities

• Modeling & Technical Execution:
  • 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.
• Execution & Collaboration:
  • 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, and to translate marketplace problems into data science solutions.
  • Lead the design and analysis of experiments (e.g., A/B tests, switchback) for your projects, and interpret complex model results and experimental outcomes with a focus 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.
• Mentorship & Influence:
  • Mentor DS I and DS II scientists, providing technical guidance, reviewing code, analyses, and models, and supporting their growth in analytical and modeling skills.
  • Influence technical decisions within the team regarding modeling choices, validation strategies, and tooling through well-reasoned arguments and expertise.
  • Drive improvements to team standards, data science best practices, and analytical rigor; take ownership of specific team practices or technical components (e.g., a feature store component, leading experimentation reviews).
  • Educate stakeholders on the capabilities and limitations of data science models, and clearly explain complex methodologies and findings to both technical and non-technical audiences.
  • Participate actively in recruiting, providing high-quality, graded interview feedback for candidates up to this level.

Requirements

• 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.

Desired Characteristics

• Experience developing, evaluating, or optimizing models or bidding algorithms for RTB environments.
• Experience working with a cloud platform like GCP/AWS/Azure, with emphasis on GCP and the Vertex AI platform.
• Experience with ML pipeline and orchestration tools such as TFX, Kubeflow, or Airflow.
• Familiarity with other programming languages such as Java and Go.
• Experience working in digital media, marketing technology, or advertising technology, especially in marketplace, auction, or exchange systems.
• Experience supporting and improving production ML models beyond their initial deployment.
• Experience mentoring junior data scientists.

Pay

$143,650 - $160,550 a year

Benefits

• Medical, dental, and vision coverage
• 401k retirement plan
• Equity
• Additional benefits as outlined here

Company Values

• We are One: We are one team with no exceptions. We are unified by a shared mission, respecting and caring for our colleagues to cultivate an inclusive culture.
• We are Customer Centric: We innovate on behalf of our customers, understanding and listening to them to build great products that solve their problems. We manage expectations clearly and honestly, acting as a trusted partner with integrity.
• OpenX is Ours: We are all owners of OpenX, taking ownership to drive results and staking our personal and professional reputations on the excellence of our work.
• We are an Open Book: We share knowledge eagerly, teach others, and learn through questions and constructive feedback.
• We Evolve Fast: We take responsible risks, learn from mistakes, recognize and repeat success, seek feedback, adapt quickly, and tackle growth with urgency.

Company Traits

• Humble: Ideal team players are humble, demonstrating integrity, putting team success above their own, and valuing collective achievements. They are open to coaching and committed to continuous learning.
• Driven: Ideal team players are results-driven, motivated, and curious. They proactively solve problems, take initiative, and continuously seek improvement.
• Smart: Ideal team players possess intellectual acumen to understand organizational and industry complexities. They are interpersonally intelligent, good communicators, and exemplify sound judgment to foster collaboration.