Staff Data Scientist - Query Tools & Data Warehouse at Pinterest

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Staff Data Scientist - Query Tools & Data Warehouse Pinterest. Data is the backbone of both innovation and decision-making at Pinterest. We have over an exabyte of data, 400k+ data tables, and 1000+ employees issuing 3M+ queries to our data warehouse per month, using data for numerous purposes from ML model training to forecasting to opportunity sizing to decision-making across sales, finance, marketing,engineering, product, design, analytics, and data science..  . The tools we have to access and derive insights on this data play a crucial role in what insights can be derived and how quickly, how the data is interpreted, and whether the data can be trusted. This layer between the customers and the data plays a crucial role in the productivity of our employees and the quality and speed of decisions that are made on the data. In this role, you will shape how effectively customers can derive insights from the data, from innovations in how we bring natural language to queries, to mechanisms to generate visualizations, to approaches to derive insights on sub-samples of data, and more. You will be a key member of an organization of talented data scientists innovating on data platforms and tools across the company.. We are looking for an experienced and highly capable Data Scientist to help us drive the next step function of analytical velocity at Pinterest..  . What you’ll do:. Apply data science and analytics. to identify opportunities to improve our data warehouse and query tools -- from visualizations to table structure to query improvements to warehouse design, 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 with which employees interact with data through our in-house tooling and data warehouse.. 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 data warehouse and query tools 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 query tools and data warehouse..  . 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.. A mastery of SparkSQL/Presto/Hive.. In-depth understanding of data warehousing, and big data-query tooling.. 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 per half and therefore can be situated anywhere in the country. .  . #LI-NM4. #LI-REMOTE