Staff Data Scientist, Ads Delivery

About Pinterest:

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 were 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 others unique experiences and embrace theflexibility to do your best work. Creating a career you love Its Possible.

At Pinterest AI isnt just a feature its a powerful partner that augments our creativity and amplifies our impact and were looking for candidates who are excited to be a part of that. To get a complete picture of your experience and abilities well explore your foundational skills and how you collaborate with AI.

Through our interview process what matters most is that you can always explain your approach showing us not just what you know but how you think. You can read more about our AI interview philosophy and how we use AI in our recruiting process here.

We are looking for a Staff Data Scientist for our Ads Delivery. You will shape the future of people-facing and business-facing products we build at Pinterest. Your expertise in quantitative modeling experimentation and algorithms will be utilized to solve some of the most complex engineering challenges at the company. 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 uplevel our product development teams while introducing greater scientific rigor into the real world products serving hundreds of millions of pinners creators advertisers and merchants around the world.

What youll do:

• Develop a deep nuanced understanding of the Pinterest ads delivery quantifying full funnel opportunities and risks.
• Lead projects on:
• Pinner LTV
• Tradeoff between ads and organic engagement
• Ads Delivery opportunities across different funnel stages
• Design and productionize robust scalable ML and evaluation frameworksspanning forecasting recommendation and causal inference.
• Advocate for best-in-class experimentation instrumentation and metric design; bridge the gap between short-term proxy metrics and long-term business impact.
• Collaborate across disciplinesProduct Engineering Research Business and Designtranslating complex data questions into actionable business insights.
• Mentor and guide junior and senior scientists fostering intellectual curiosity and driving technical excellence.

What were looking for:

• 10 years of hands-on experience in web-scale data environments with a track record of solving hard ambiguous problems in product engagement or ecosystem analytics.
• Bachelors/Masters degree in a relevant field such as Computer Science or equivalent experience.
• Deep expertise in: Machine Learning (recommendation ranking prediction experimentation) Statistical Modeling & Causal Inference (observational and experimental data) Product analytics/strategy (beyond dashboards: root cause goaling design collaboration) Programming in Python/R and advanced SQL/Spark.
• Strong product intuitionability to scope question and design the right solutions for ill-defined high-impact business problems.
• Scientific rigor and healthy skepticism: You challenge assumptions find flaws and drive towards robust reproducible outcomes.
• Exceptional communication: You make the complex simple and can influence both technical and non-technical audiences.
• Track record mentoring and growing data talent at the staff/senior IC level.
• Cross-functional leadership and the ability to align competing interests towards shared goals.

Relocation Statement:

• This position is not eligible for relocation assistance. Visit our PinFlex page to learn more about our working model.

In-Office Requirement Statement:

• We recognize that the ideal environment for work is situational and may differ across departments. What this looks like day-to-day can vary based on the needs of each organization or role.
• This role will need to be in the office for in-person collaboration 1-2 times/quarter and therefore can be situated anywhere in the country.

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