Data Scientist II, ML Infrastructure

What you'll do

• Translate research-grade data science workflows into production ML pipelines using Airflow, WandB, and Ray.
• Productionize causal inference methods like propensity scoring and IPW to address complex measurement questions.
• Build self-serve tooling to enable non-experts to derive rigorous causal insights at scale.
• Develop data-driven frameworks for feature importance and content deindexing using platform metadata.
• Design and build centralized ML platform tools to improve model evaluation, trust, and creation.
• Identify opportunities with engineers and product teams to improve metrics and measurement methods.
• Establish reusable patterns and rigorous methodological standards for the broader ML organization.

What we're looking for

• Bachelor's or Master's degree in a relevant field such as Computer Science, or equivalent experience.
• 2+ years of hands-on experience as an applied scientist, ML engineer, research scientist, or software engineer.
• Significant experience in production machine learning environments.
• Strong Python skills and experience with PyTorch or equivalent deep learning frameworks.
• Familiarity with distributed compute systems such as Spark or Ray.
• Deep knowledge of machine learning theory and first principles.
• Proficiency in software development best practices including version control, code review, and reproducible pipelines.
• Experience with workflow management tools like Airflow, Prefect, or Jenkins for pipeline orchestration.