data sciencemachine learningpythonsqltensorflowpytorchgcpvertex aiab testingcausal inferencefeature engineeringmodel deploymentbigquerydataflowkubeflowairflowdigital advertisingrtbml pipelinesmentorship
Key details
Salary
$144K – $161K
Employment type
Full-time
Seniority
Senior
Years experience
5-10
Location
New York, US
Full job description
OpenX is hiring a Data Scientist III to independently own medium-to-large data science projects end-to-end, including problem formulation, research, deployment, and maintenance of production models. Responsibilities include building production-ready models addressing bidding, yield modeling, relevance, prediction systems, experimentation, and causal measurement at exchange scale. The role requires expertise in machine learning and statistics, writing efficient code, designing validation frameworks, troubleshooting, collaborating with product and engineering teams, mentoring junior scientists, and influencing technical decisions. Candidates must have a B.S./M.S. with 5+ years or Ph.D. with 2+ years relevant experience, strong Python and SQL skills, experience with ML frameworks (TensorFlow or PyTorch), and strong communication skills. Experience with cloud platforms (preferably GCP and Vertex AI), ML orchestration tools, and digital advertising technology is desired. Salary range is USD 143,650 - 160,550 per year plus bonus, equity, and benefits. Location: New York, NY.
What you'll do
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
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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
