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Remote senior ML role with a common title but niche adtech and Databricks/Azure requirements.
High — requires specific advertising/audience activation experience plus Databricks/Snowflake/Azure expertise, limiting industry portability.
High — explicit 8–9 years, required adtech background, and mandatory Databricks/Snowflake/Azure stack.
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Deliver scalable ML services for audience segmentation and activation on terabyte-scale data across multiple channels including DSPs, social, and CTV.
Own end-to-end ML lifecycle including prototype conversion, data pipeline engineering on Databricks/Spark, model tracking, CI/CD, automated retraining, drift monitoring, and deployment on Azure.
Lead system design, mentor engineers, and ensure scalability, latency, cost efficiency, and data privacy compliance across production ML infrastructure.
8–9 years of hands-on ML engineering experience shipping and operating production ML systems at scale.
Prior experience in media, advertising, or audience activation domain essential.
Proficiency in Databricks (MLflow, Spark), Snowflake, production Azure services (ADLS, Azure ML, Azure DevOps, AKS), Python, and SQL at scale.
Bachelor's degree in Computer Science or engineering with strong quantitative foundations.
Experienced with complex data science to production ML service conversion in large-scale, multi-channel advertising environments.
Strong applied statistics skills relevant to forecasting, causal inference, and experimental design to support audience modeling.
Skilled in managing privacy-by-design, cost/latency trade-offs, and cross-functional communication with non-technical stakeholders in distributed teams overlapping US working hours.