Sr. Data Scientist

Pinterest Job Advertisements•San Francisco, CA

4d•$240,000 - $287,749•Hybrid

About The Position

Millions of people around the world come to our platform to find creative ideas, dream about new possibilities and plan for memories that will last a lifetime. At Pinterest, we’re on a mission to bring everyone the inspiration to create a life they love, and that starts with the people behind the product. Discover a career where you ignite innovation for millions, transform passion into growth opportunities, celebrate each other’s unique experiences and embrace the flexibility to do your best work. Creating a career you love? It’s Possible.

Requirements

Master’s degree (or its foreign degree equivalent) in Data Science, Statistics, Computer Science, Engineering (any field), or closely related quantitative discipline and four (4) years of experience in the job offered or in any occupation in related field, OR Bachelor’s degree (or its foreign degree equivalent) in Data Science, Statistics, Computer Science, Engineering (any field) or closely related quantitative discipline and six (6) years of progressively responsible experience in the job offered or in any occupation in related field.
(1) Statistics
(2) Experimentation (design and execution)
(3) SQL, Hive, and Python
(4) Pandas, pymc, and scikit-learn
(5) R
(6) Databricks
(7) Bayesian Regression
(8) XGBoost
(9) K-Means
(10) Random Forest
Any suitable combination of education, training and experience is acceptable.
Part-time telecommuting is an option.
Hybrid work from Pinterest office in San Francisco, CA.

Responsibilities

Conduct deep strategic analysis to answer core questions such as: How do we assess the trade-off between metrics change? How should we evaluate overall impact from changes in one component of the ads ecosystem?
Determine opportunity sizing and analysis. Should Pinterest adjust programmatic ad load based on?
Write clear, actionable analyses that help teams identify areas of improvement and investment.
Build segmentation models to assess supply to inform pricing strategy.
Improve decision velocity and quality using data scientist tool kit: experimentation, causal inference techniques, etc.
Design measurement strategy, advise on experimentation best practices, identifying flaws in experiment practices and results; building tools for experiment analysis etc.
Creating and tracking success metrics.
Identify the right measures of success for engineering teams and help them track those metrics.
Break down high-level metrics into actionable segments.
Lead and mentor the scope of work for data scientists in the same area, demonstrating high-quality output of both yourself and others for whom you are responsible.
Provide continuous and candid feedback, recognizing individual strengths and contributions and flagging opportunities to improve performance.