We are looking for a Staff Data Scientist to join our Growth org. You will collaborate on a wide array of product and business problems with a diverse set of cross-functional partners across Product, Engineering, Design, Research, Product Analytics, Data Engineering and others. The results of your work will influence and up-level our product development teams in the Discovery org while introducing greater scientific rigor into the real world products serving hundreds of millions of pinners, creators, advertisers and merchants around the world.

What you’ll do:

• Deep strategic analysis to Answer growth ecosystem questions such as how to drive long term user engagement and how to balance monthly active user growth and overall user engagement.
• Product recommendations. Clearly communicate recommendations to product and engineering leadership on how we can evolve our internal strategy to address shortcomings observed through deep analysis. 
• Opportunity sizing and analysis. Write clear, actionable analyses that help teams identify areas of improvement to our growth strategies. How do we segment users and provide more personalized experiences for different segments users?
• Experimentation: Evolve our experimentation capabilities and tools to evaluate the changes we make to our growth levers. Advise on experimentation best practices; identifying flaws in experiment practices and results; building tools for experiment analysis; collaborative design of new multicell experiment frameworks, counterfactual logging, etc.
• Creating and tracking success metrics. Identify the right measures of success for product teams and help them track those metrics. Own the full lifecycle of those metrics from logging requirements, metrics definition, prototype pipelines, and improvements. 
• Leadership: Lead and mentor the scope of work for at least one other individual, 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.

What we’re looking for:

• 8+ years of combined post-graduate academic and industry experience applying scientific methods to solve real-world problems on web-scale data.
• Proven ability to apply scientific methods to solve real-world problems on web-scale data.
• Expertise in at least one scripting language (ideally Python/R).
• Proficiency in SQL/Hive.
• Strong business and product sense: delight in shaping vague questions into well-defined analyses and success metrics that drive business decisions.
• Excellent communication skills: able to lead initiatives across multiple product areas and communicate findings with leadership and product teams.
• Experience leading key technical projects and substantially influencing the scope and output of others.
• Leadership. Comfortable to leverage expertise to influence others instead of authority. Resourceful and being a resource to solve Pinterest’s business problems in the face of constraints.
• Strong business / product sense. Ability to ask the right questions (why the work is important, what decisions will help inform it, what an 80% solution will look like). Can explain why past work done was important and justify decisions made. Wants work to be practically applicable and have product impact in the short- to medium-term (< 6 month).
• Data skepticism and curiosity. Doesn't think data or a model can solve every problem; digs into anomalies and wants to understand them. Identifies issues in datasets.
• Strong communication skills. Explains work and thought processes clearly and concisely.
• Willingness to dive in and do work, even if it's not the most glamorous, to get the job done. Can work with dirty data sets, do manual labeling, and improvise as needed to work around problems. Works quickly and efficiently, doesn't think any work is below them.
• Interest in repeatable analysis. Seeks to automate where possible, strives for solutions that can be repeated on new datasets.
• Desire for simplicity. Don't strive for perfection when a simple solution will do the job. Recognizes that linear models and plain old counting can be very powerful.
• Technical proficiency. Can write efficient SQL queries for many-to-many datasets without erroneous duplication; familiar with left outer join and why it would be useful. Able to write basic code but needn't write elegant solutions to the sorts of algorithm questions often found in coding interviews.

What skills are ideal but not required?

Exposure to one or more of the following areas is beneficial. Ideal candidates would have exposure to 2+ of these areas and deep familiarity in 1 or more areas.

• Strongly desired: experience working in Growth function, in particular, experience working on SEO, logged out user experience, user activation and sharing/messaging.
• Strong Experimentation background. Experience with working on experiments with network effects.
• Statistical rigor. Experience with causal inference projects.
• ML modeling, ideally with biased / incomplete training data. Has built models end to end (feature design, feature selection, training data, implementation, iteration) and can intelligently discuss trade-offs / decisions at each step.

This position is not eligible for relocation assistance.

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