• You are experienced with the lifecycle of machine learning, data science, data and ML pipeline design, and have a deep understanding of how they can be deployed and orchestrated in a cloud-based infrastructure
• Ideally, you would have worked as a data scientist for some years and decided your passion lies in the ML ops / data engineering side of data science
• You should be hands-on and capable of implementation in addition to design
• 5+ years of experience with AWS or GCP
• 10+ years of experience in software development with Python in a production environment focused on ETL, ML, and pipeline work
• 10+ years of experience working with large datasets (working with raw logs in the order of 200 - 250TB a day. Comfortable working with databases with tables ranging from 100's of billions to trillions of rows.)
• 5+ years of experience working as a data scientist with technologies such as scikit-learn, R, SQL, H2O, Vertex AI, AWS Sagemaker, TensorFlow, PyTorch, and Spark.. building machine learning models on sparse datasets with high dimensionality
• 5+ years with predictive analytics and developing optimization algorithms
• Deep understanding of development and deployment processes with technologies including GitHub actions, containerization using Docker, and cloud services such as ECR/ECS, Lambda, and Cloudwatch
• Experience working with Airflow or another workflow orchestration tool
• Expert-level understanding of SQL in Snowflake and MySQL environments
• Experience with high-performance SQL-based analytics data warehouses such as Snowflake, BigQuery, Redshift, Vertica, or Netezza
• MS or equivalent combination of education and experience in Computer Science, Engineering, Information Systems, or quantitative science (Physics, Math, Computational Biology, Operations Research, etc.)
• Strong verbal and written communication skills
• Ad tech experience (SSPs, DSPs, Analytics, DMPs, CDPs)

Desirable

• We’re looking for an experienced architect and engineering leader to spearhead our machine learning operations, with a focus on optimizing the deployment and management of ML models
• You will design and implement systems to support our data science initiatives in a low latency high volume (500B - 1T transactions a day) production environment
• We currently have multiple ML implementations that we’d like to unify into a common framework to ensure security, support reusability and observability, and expedite the ability to make changes
• Architect, deploy, and manage a software framework to securely handle data science workflows not limited to machine learning in production-grade environments
• Facilitate seamless integration of ML models into operational pipelines between engineering and data science
• You will partner with our data scientists to set best practices, automate data science approaches, architect solutions, and deploy and monitor production workloads
• Design, implement, and maintain scalable, efficient ML pipelines on AWS and GCP
• Design, implement, and maintain CI/CD pipelines to automate the continuous integration and delivery of ML models
• Monitor the performance of ML models and implement improvements
• Ensure high availability and fault tolerance of the ML infrastructure
• Optimize the performance and cost-efficiency of ML systems
• Perform code reviews; maintain and champion ML ops and engineering best practices
• Manage a team of data / ML ops engineers
• Recruit, mentor, and grow machine learning engineering team
• Contribute to and evolve our current data science initiatives, which include cookieless targeting, format optimization, creative optimization, traffic throttling, and real-time bidding management