Data Scientist, Digital

Floor & Decor is building better ways to help customers discover products and complete projects across flooranddecor.com, the FND apps, and omnichannel shopping experiences. In this onsite role in Atlanta, GA, the Data Scientist, Digital Machine Learning (ML) will develop, deploy, and maintain ML models that support personalization, recommendations, and search driven by customer intent signals. The work includes moving models into production and improving them continuously through measurement and monitoring.

What you’ll do

• Build and maintain recommendation models across site and app, including similar products, “complete the project” recommendations, and continue-your-project experiences.
• Develop session-based models that infer shopper intent (project type, style, DIY vs. Pro, and 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.
• Improve product search through query classification, synonyms, and relevance evaluation.
• Apply LLMs and embeddings to enrich product attributes that improve search, filtering, and recommendations, including quality checks on technical specifications and support evaluation of LLM-powered features by building evaluation sets and reviewing output quality.
• Generate digital propensity and intent scores (for example, sample-to-purchase conversion and Pro identification) for site, app, and marketing systems using enterprise customer segments from Customer Analytics & Insights instead of creating parallel segmentation.
• Contribute to shared ML assets that carry across Ecommerce platform and vendor changes, and support vendor evaluations using Floor & Decor data.
• Help bring personalization, search, and recommendations to the upcoming PRO app using Pro purchase history, project patterns, and loyalty data to drive adoption, engagement, and share of wallet for Pro customers.
• Deploy and operate models on Databricks following team standards, monitoring performance, data quality, and drift, and retraining as needed.
• Partner with Data Engineering, Product, and Engineering to ensure data and event tracking support model training and measurement.
• Design and analyze A/B tests for model-driven features, measuring incremental impact on conversion, average order value, and engagement (with standard digital performance reporting owned by Ecommerce Analytics).
• Communicate results, tradeoffs, and recommendations to product teams and business stakeholders in a clear, actionable way.
• Maintain documentation of methodologies, model assumptions, and limitations, and participate in code and design reviews.
• Stay current on industry trends and best practices in personalization, search, recommender systems, and applied data science.
• Follow Privacy Preferences, Responsible AI, and Security protocols, including customer consent and preference requirements for personalization.

Requirements

• Bachelor’s degree in Computer Science, Statistics, Mathematics, Engineering, Economics, or a related quantitative field (advanced degree a plus). Relevant experience may substitute for education based on 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 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 (examples include Claude Code, GitHub Copilot, and Cursor).

Preferred

• Databricks on Azure, PySpark, and CI/CD for ML.
• Experience extending, evaluating, or migrating commercial search or personalization platforms (examples include Bloomreach, Constructor, Algolia, Dynamic Yield, and 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.

Technologies

• Python, SQL, Databricks, PySpark, CI/CD
• LLMs, embeddings
• Claude Code, GitHub Copilot, Cursor
• Bloomreach, Constructor, Algolia, Dynamic Yield, Salesforce

Benefits

• Bonus opportunities and career advancement opportunities at every level
• 401k with company match
• Employee Stock Purchase Plan
• Referral Bonus Program
• Medical, Dental, Vision, Life, and other Insurance Plans (subject to eligibility criteria)
• Paid vacation and sick time for eligible associates
• Paid holidays plus a personal holiday
• Paid Volunteer Time Off that starts on Day 1

Travel and working conditions

• Travel may be required, typically less than 10%, including occasional air and car travel.
• Role is based in the Store Support Center in Atlanta, GA.
• Work is primarily sedentary, involving extended periods of sitting at a computer, with occasional walking or standing; environment is typically quiet to moderate.
• Sedentary work requirements include the ability to exert up to 10–20 lbs occasionally and negligible force frequently.