Data Scientist III

OpenX; New York (NY); On-site; USD 120,000 - 190,000; Full time

Job summary

OpenX in New York, NY is seeking a Data Scientist III to own medium-to-large data science projects end-to-end, from problem formulation to production deployment. You will build production‑ready models and analyses that solve real marketplace problems, partner with product and engineering, and mentor junior scientists.

The role covers bidding and yield modeling, relevance and prediction systems at exchange scale, experimentation and causal measurement, and the feature engineering, validation, and

Qualifications

• Experience leading medium-to-large data science projects end-to-end.
• Strong background in ML methods and statistics.
• Ability to deploy models to production and monitor performance.

Responsibilities

• Own end-to-end data science lifecycle for complex projects.
• Collaborate with product and engineering to ship models.
• Mentor junior scientists and communicate results.

Skills

Data science

Machine learning

End-to-end lifecycle

Production models

Tools

Vertex AI pipelines

Dataflow

BigQuery

Job description

Company at a Glance

OpenX is focused on unleashing the full economic potential of digital media companies. We do this by making digital advertising markets and technologies that are designed to deliver optimal value to publishers and advertisers on every ad served across all screens.

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.

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.
<