Senior Data Scientist — ML Platforms & Causal Inference

Pinterest; San Francisco (CA); Hybrid; USD 114,000 - 235,000; Full time

Job summary

Pinterest is seeking an experienced Data & Applied Scientist to advance ML capabilities and productionize ML workflows at scale. You will translate research-grade pipelines into production systems and collaborate with ML engineers and product teams to improve tooling, metrics, and discovery of insights.

With a focus on causal inference, you will build self-serve tooling and centralized platform components to influence model quality and business outcomes across the company.

Qualifications

• 2+ years of hands-on experience as an applied scientist, ML engineer, or equivalent with ML production exposure.
• Strong Python skills and experience with PyTorch or similar DL frameworks.
• Familiarity with distributed compute (Spark, Ray) and production tooling.
• Proficiency in software development best practices and reproducible ML pipelines.

Responsibilities

• Translate research-grade DS workflows into production ML pipelines using Airflow, WandB & Ray with reusable patterns.
• Apply and productionize causal inference methods (propensity scoring, IPW, TMLE) to address high-stakes measurement questions.
• Partner with ML engineers and product teams to improve tooling, metrics, and measurement methods.
• Leverage metadata and signals to build data-driven frameworks that improve efficiency and speed.
• Design and build centralized ML platform tooling for feature/model creation, evaluation, and trust.

Skills

Python

PyTorch

Spark

Ray

Airflow

WandB

Prefect

Jenkins

Version control

Distributed compute

Reproducible pipelines

Education

Bachelor's/Master's in CS or equivalent

Tools

Airflow

Jenkins

Prefect

Job description

Pinterest is seeking an experienced Data & Applied Scientist to advance ML capabilities and productionize ML workflows at scale. You will translate research-grade pipelines into production systems and collaborate with ML engineers and product teams to improve tooling, metrics, and discovery of insights.

With a focus on causal inference, you will build self-serve tooling and centralized platform components to influence model quality and business outcomes across the company.