Data Scientist III, Global Supplier Technology

Wayfair Data Science is the engine that powers an enterprise obsessed with data. Global Supplier Technology (GST) builds the platforms, data and science that tens of thousands of suppliers and Wayfair's internal teams use to list, price, merchandise and grow products on Wayfair. Its scope includes PartnerHome, catalog and merchandising, pricing and profitability, advertising and B2B. GST Data Science works directly alongside these product areas. We partner with Product, Engineering, ML Science and Commercial leaders to size opportunities, measure what actually works, and point technology investment at the highest-return problems.

Within GST, the Strategic Demand Optimization program groups products customers see as true substitutes into Demand Huddles, giving us a customer-centric map of demand. We use it to find the best product in each choice set, spot assortment gaps, and steer demand toward proven winners.

As a Data Scientist III on this team, you'll turn that map into decisions. You'll define how we measure and rank huddles, figure out why products win or lose within them, and show whether steering demand creates real value or just moves share around. This is an early-stage space with open questions, and you'll set the analytical direction rather than inherit it. To succeed, you'll need strong analytical and technical skills, sound experimental judgment, and the ability to influence merchants, product leaders and scientists.

What You'll Do

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Define and own the strategic agenda to unlock insights and guide the business.

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Run open-ended exploratory data analysis to identify new ideas and opportunities.

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Champion the use of generative AI tools to accelerate analysis, automate repetitive tasks, and enhance insight generation.

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Develop how we measure and prioritize Demand Huddles, and understand what drives product performance within them.

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Design and drive A/B tests of how we steer demand (e.g., ranking boosts, badges, merchandising placements) to measure true incremental value.

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Proactively monitor our data to identify potential issues, determine root causes, and resolve.

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Build new tools and pipelines that allow us to be more productive and efficient.

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Work closely and build productive relationships with our product, engineering, merchandising and machine learning stakeholders.

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Proactively identify areas where advanced analytical efforts can produce a strong value add and work them into the road map.

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Communicate key insights and recommendations to cross-functional executive leaders across the organization.

What You'll Need

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Bachelors in computer science, engineering, math, statistics, economics, or other quantitative discipline; Masters preferred.

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4+ years work experience in a relevant field (in addition to any graduate education).

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Analytics or Data Science experience with e-commerce or fintech companies is a strong plus.

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In-depth experience with SQL (aggregate functions, query optimization, stored procedures, string parsing, GBQ/Spark, etc.), required.

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In-depth experience with python & python tools (Jupyter notebooks, functions, pandas, sklearn, matplotlib, etc.), required.

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Experience with experimental design (A/B tests) and statistical analysis to drive business decision making, highly preferred.

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Experience with Looker (or similar BI tools) for reporting and dashboarding.

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Knowledge and experience using machine learning algorithms/techniques (decision trees, random forest, deep learning, etc.) within a business context, highly preferred.

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Strong written and verbal communication with a team player, highly collaborative attitude.

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Ability to collaborate and proactively communicate with stakeholders.

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Proven track record of taking ownership and driving results. Thrive in a fast-paced environment.

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Strong business acumen, analytical skills, and technical abilities along with problem-solving skills.

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Demonstrated success influencing senior level stakeholders on strategic direction based on recommendations backed by in-depth analysis.

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Prior experience proactively identifying areas where analytical efforts can produce a strong business value add and setting the analytics roadmap.

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Able to manage multiple projects and deliver against aggressive goals.