ML Ops Engineer-8013

Comcast-posted 2 months ago
Full-time
Remote • Philadelphia, PA
Telecommunications
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Comcast Advertising is driving the TV advertising industry forward, from delivering ads to linear and digital audiences to pioneering the tech that makes it possible. We help brands connect with their audiences on every screen using advanced data, technology, and premium video content. Our media sales division helps local, regional, and national brands reach potential customers through multiscreen TV advertising. Our ad tech division FreeWheel provides comprehensive adtech that makes it easier to buy and sell premium video advertising across all screens, data types, and sales channels.

  • Contribute to a team responsible for developing machine learning (ML) algorithms, models, and data pipelines for digital and linear advertising.
  • Use Keras, TensorFlow, PyTorch, Spark, Python, and Scala within an Agile development environment.
  • Deploy ML models using AWS services, including EMR and Sagemaker.
  • Ensure ML model governance and measure ML model drifts using Python and MLFlow.
  • Build and maintain CI/CD pipelines for ML use cases using Concourse, Github and Terraform.
  • Use the Databricks platform for data analytics.
  • Create feature stores using Databricks features including Feature Store, Delta Tables and Online Table Store.
  • Identify data drift and model degradation over time and create necessary alerts to proactively address issues with deployed models.
  • Collaborate with data engineers, data scientists, and technical leads to develop and deliver cloud-based solutions to support scalable and reliable data science workflows.
  • Partner with Data Engineering to produce data pipelines.
  • Implement a robust system for measuring and optimizing the quality of deployed algorithms and models.
  • Design and implement enterprise ML Ops.
  • Collaborate with data scientists to help integrate ML Ops into the model development process.
  • Assist in setting best practices to support scalable data science solutions.
  • Master's degree (or foreign equivalent) in Computer Science, Statistics, Data Science, Analytics, or any related technical or quantitative field.
  • One (1) year of experience developing machine learning (ML) algorithms, models, and data pipelines using Keras, TensorFlow, PyTorch, Spark, and Python within an Agile development environment.
  • Experience deploying ML models using AWS services, including EMR and Sagemaker.
  • Experience ensuring ML model governance and measuring ML model drifts using Python and MLFlow.
  • Experience building and maintaining CI/CD pipelines for ML use cases using Terraform and Github.
  • Experience using the Databricks platform for data analytics.
  • Experience creating feature stores using Databricks features including Feature Store, Delta Tables, and Online Table Store.
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