Data Scientist, Digital

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* Purpose:

** The Data Scientist, Digital Machine Learning (ML), develops, deploys, and maintains ML models that support Floor & Decor's digital customer experience across , FND apps, and omnichannel digital shopping tools. Areas of responsibility include personalization and recommendations, product search and discovery, product data enrichment, and customer intent signals. Working cross-functionally with Ecommerce Product, Engineering & Analytics, Data Engineering, Merchandising, and Digital Marketing, this role advances models from development through production deployment, monitoring, and continuous improvement.

The position adheres to the team's technical standards and partners closely with the Senior Data Scientist, Digital, to deliver measurable improvements in how customers find, evaluate, and select products.  
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* Minimum Qualifications:

*** Bachelor's degree in Computer Science, Statistics, Mathematics, Engineering, Economics, or a related quantitative field (advanced degree a plus); relevant experience can be substituted for the required education on the basis of one calendar year of experience for one academic year of education.
* 3+ years of experience in data science or machine learning, including hands-on contribution to at least one model deployed into a production system, or an equivalent combination of education and experience sufficient to perform the essential functions of the job, as determined by the company.
* Experience building machine learning for live, customer-facing digital products such as search, recommendations, ranking, personalization, or ads, in e-commerce, marketplaces, media, or similar environments.
* Strong proficiency in Python and SQL, writing clean, tested, production-ready code.
* Solid foundation in machine learning, including classification, ranking, and recommendation approaches.
* Familiarity with ML lifecycle practices, including automated training and scoring pipelines, deployment, and monitoring.
* Experience designing and analyzing A/B tests and interpreting results for business partners.
* Proficiency with AI-assisted development tools (e.g., Claude Code, Git Hub Copilot, Cursor) to accelerate exploration, modeling, and engineering.  
** Preferred:
*** Databricks on Azure, PySpark, and CI/CD for ML.
* Experience extending, evaluating, or migrating commercial search or personalization platforms (e.g., Bloomreach, Constructor, Algolia, Dynamic Yield, Salesforce).
* Experience with deep learning recommenders, embeddings, or LLMs applied to product, content, or customer data.
* Experience with clickstream and customer behavioral data.
* Ecommerce or omnichannel retail experience, ideally in high-consideration, project-based categories.  
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* Competencies:

*** Analytical Problem-Solving
* Technical Communication
* Organization & Adaptability
* Collaboration Across Product Teams
* Integrity, Humility & Teamwork
** Essential Functions /

Key Responsibilities:

*** Build and maintain recommendation models across site and app, including similar products, "complete the project" recommendations (e.g., tile with the setting materials, tools, and underlayment the project requires), and continue-your-project experiences.
* Develop session-based models that infer shopper intent (project type, style, DIY vs. Pro, stage in the purchase journey) for anonymous and infrequent visitors.
* Rank content, offers, and modules across the home page, category and product pages, and app.
* Contribute to product search improvements, including query classification, synonyms, and relevance evaluation.
* Apply LLMs and embeddings to enrich product attributes that improve search, filtering, and recommendations, with quality checks on technical specifications, and support evaluation of LLM-powered features by building evaluation sets and reviewing output quality.
* Produce digital propensity and intent scores (e.g., sample-to-purchase conversion, Pro identification) for the site, app, and marketing systems, leveraging enterprise customer segments from Customer Analytics & Insights rather than building parallel segmentation.
* Contribute to shared ML assets that carry across Ecommerce platform and vendor changes, and support vendor evaluations with analysis on Floor & Decor data.
* Bring personalization, search, and recommendations to Floor & Decor's upcoming PRO app, using Pro purchase history, project patterns, and loyalty data to drive app adoption, engagement, and share of wallet with Pro customers.
* Deploy and operate models on…