Data Scientist II, ML Infrastructure

• Translate research-grade DS workflows (e.g., proxy metrics, staleness models) into production ML pipelines using Airflow, WandB & Ray while establishing reusable patterns for other teams.
• Apply and product ionize causal inference methods using the production ML stack (propensity scoring, IPW, TMLE) to address high-stakes measurement questions beyond experimental capabilities. Build self-serve tooling to empower non-experts to derive rigorous causal insights at scale.
• Partner with ML engineers and product teams to identify opportunities for improved tooling, metrics, and measurement methods, unlocking step-change improvements in model quality and business outcomes.
• Leverage Pinterest's rich metadata and engagement signals to build data-driven frameworks, from feature importance to content deindexing, that improve platform efficiency and speed.
• Design and build centralized ML platform tooling to improve feature and model creation, evaluation, and trust, including production systems that operate daily at scale across all models.

• 2+ years of hands-on experience as an applied scientist, ML engineer, research scientist or software engineer, with significant ML production experience.
• Strong Python skills; experience with PyTorch or equivalent deep learning frameworks; familiarity with distributed compute (Spark, Ray). Ray specifically is a strong plus.
• Enthusiasm for building tools and platforms that multiply the impact of an entire ML organization; not just solving one-off problems.
• Deep ML theory knowledge with extremely strong fundamentals that can help us reason about ML models from first principles.
• Proficiency in software development best practices including version control, code review, and reproducible ML pipelines.
• Experience with workflow management tools (Airflow, Prefect, Jenkins, or similar) for reliable ML pipeline orchestration.
• Bachelor’s/Master’s degree in a relevant field such as Computer Science, or equivalent experience.

Demonstrates expertise in building and product ionizing machine learning pipelines, with a strong foundation in ML theory and practical experience in developing tools that enhance organizational impact. Proficient in Python and familiar with distributed computing frameworks, capable of designing centralized ML platforms for efficient model management.

• Machine Learning Production Experience
• Python Programming
• Workflow Management Tools (Airflow, Prefect, Jenkins)
• Deep Learning Frameworks (PyTorch, Ray)
• Causal Inference Methods

• Machine Learning
• Causal Inference
• Python
• Deep Learning
• Version Control
• Reproducible ML Pipelines
• Feature Engineering
• Model Evaluation
• Data-Driven Frameworks
• Centralized ML Platform Design

• Problem Solving
• Collaboration
• Enthusiasm for Tool Building

• Applied Scientist
• ML Engineer
• Research Scientist
• Software Engineer
• Computer Science

• Airflow
• WandB
• Ray
• Spark
• Prefect
• Jenkins