Data Scientist

Fox Corporation•Los Angeles, CA

3d•Remote

About The Position

As part of the Data Engineering team at Fox Cable Network Services, LLC, the Data Scientist (MLOps Engineer) will focus on deploying, managing, and scaling machine learning models within Fox’s AWS and Databricks environments. This role is crucial for ensuring Fox’s ML models are deployed reliably, maintained efficiently, and deliver value across the organization. The Data Scientist will work effectively with multiple internal stakeholders across diverse groups such as Fox Sports, Fox News Media, Fox Entertainment, and Fox Television Stations to support a wide array of Data Science and Data Engineering initiatives. Telecommuting is permitted from any location in the U.S.

Requirements

Master’s degree in Data Science, Computer Science, Information Technology, Engineering or related field.
Two (2) years of experience in data science or related experience in MLOps/DevOps.
Experience deploying and maintaining ML models in production.
Experience building CI/CD pipelines for ML models using tools such as Git and Jenkins.
Experience in ML frameworks such as TensorFlow, PyTorch and scikit-learn.
Experience in SQL, Python, Docker, and Kubernetes.
Knowledge of monitoring and logging tools used to track model performance and data quality such as Prometheus, Grafana and CloudWatch.
Experience with A/B testing and experiment design.
Experience with product analytics and working with event-level data.
Experience in Databricks and AWS services such as S3, EC2, SageMaker and Lambda.

Responsibilities

Design, implement, and maintain robust, scalable ML pipelines for the production and deployment of machine learning models.
Collaborate closely with other data scientists, data engineers, and DevOps to ensure seamless integration and performance of models in AWS and Databricks environments.
Build automated workflows for model deployment, monitoring, and retraining, ensuring high availability and reliability.
Develop processes for tracking, logging, and versioning of models to support reproducibility and auditability.
Implement monitoring solutions to track model performance and data drift, providing insights to maintain and improve model quality over time.
Troubleshoot, debug, and optimize ML pipelines to improve efficiency and reduce latency.
Stay updated with the latest advancements in MLOps tools and practices, ensuring that Fox infrastructure remains cutting-edge and future-ready.