Design, build, and maintain machine learning model productionization infrastructure. These models will drive very visible product features at The Athletic, and your infrastructure is key to their success.
Work with the rest of the data science team to streamline model training, validation, and deployment.
Implement robust monitoring and alerting for model performance, drift, and data quality, ensuring our models remain accurate in production.
Cultivate an outstanding data science environment by championing best practices in ML Ops and evaluating and integrating new technologies into our data science stack.

Requirements

4-6 years of experience as a data scientist, data engineer, or machine learning engineer with a proven ability to build machine learning model pipelines and infrastructure.
Strong working knowledge of Python and SQL to pull, transform, and otherwise deal with data in all of its forms.
Proven experience with ML frameworks (e.g., scikit-learn, PyTorch) and cloud platforms (e.g., GCP, AWS, Azure).
Hands-on experience with Docker, Kubernetes, and Airflow.
Ability to drive projects with minimal guidance and prioritize high-impact work.
Strong verbal and written communication skills with ability to build cross-functional relationships.
Can explain sophisticated concepts to diverse audiences and craft compelling stories with data.

Benefits

Highly competitive, employer-contributed medical, dental, vision, basic life and disability insurance plans.
Savings accounts for medical, wellness, and childcare expenses.
401k retirement savings plan and employer match.
Paid time off including paid sick leave, 12 paid holidays, 15 days of accrued vacation to start, and up to 20 weeks of Paid Parental Leave.
Global benefits packages offer similar benefits and perks, competitive to the local market.

Applicant Tracking System Keywords

Tip: use these terms in your resume and cover letter to boost ATS matches.

Hard Skills & Tools

machine learningmodel productionizationmodel trainingmodel validationmodel deploymentPythonSQLML frameworksdata pipelinesdata quality

Soft Skills

project managementprioritizationcommunicationcross-functional collaborationstorytelling with data