Quality data labels are critical to our evaluation and machine learning efforts across at Pinterest and are often used to guide teams toward a North Star that is either difficult or noisy to define using engagement measures. However, labeling with human evaluators at scale has limitations in both cost and time - it can be time-intensive to accurately define the tasks, costly to get large volumes of labels, and slow to collect the data. We’re looking for a data scientist to join our team in pushing the boundaries of the mechanisms through which we obtain labels at scale - from statistical approaches that can better inform the labels we collect to prototypes to automatically craft prompts for LLMs to address the needed tasks. You will be a key member of an organization of talented data scientists innovating on data platforms and tools across the company.

What you’ll do:

We are looking for an experienced and highly capable Data Scientist to help us drive step function improvements in our data labeling capabilities at Pinterest. In this role, you will:

• Apply data science and analytics to identify opportunities to improve the throughput and quality of the data collection approaches used to collect labels today -- quantify the impact these improvements will have on our pace of innovation, and prototype solutions that measurably improve the outcome for the platform. 

Build a roster of high-impact analytical opportunities that improve velocity, clarity and trust in our labeled data collection solutions.

• Collaborate with customers of the platforms to understand whether data observations reflect their problems and pain-points, and with engineers to scale your successful prototypes to become integral components of the platform.
• Up-level data scientists across the organization through mentorship, partnership and constructive feedback.
• Evolve our strategy in close partnership with product and engineering leaders by building on the learnings uncovered by yourself and partners and shaping the evolution of our platforms and tools.

What we’re looking for:

• 8+ years of combined post-graduate academic and industry experience applying scientific methods to solve real-world problems on large-scale data.
• 5+ years of hands-on experience as an individual contributor using a deep understanding of scientific methods applied to data to drive business decisions.
• Proven track record of crafting high quality algorithmic code and identifying opportunities for efficiency and performance improvements through statistical methods.
• Experience with developing high quality prompts for LLMs.
• Demonstrated execution and impact on cross-functional initiatives, strong communication skills, and a track record of influencing leaders and peers using data.
• Self-propelled continuous learner who keeps up with new tools and methodologies and builds prototypes with concepts learned.
• Strong business and product sense who can shape vague questions into well-defined analyses and success metrics that drive business decisions.

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 let the type of work you do guide the collaboration style. That means we're not always working in an office, but we continue to gather for key moments of collaboration and connection.
• This role will need to be in the office for in-person collaboration 1-2 times/month and therefore can be situated anywhere in the country. 

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