Senior Data Scientist, Digital

Floor & Decor Holdings Inc.; Georgia; On-site; USD 140,000 - 210,000; Full time

Job summary

Floor & Decor Holdings Inc. is seeking a data science professional to build, deploy and maintain ML models powering the company’s digital shopping experience across flooranddecor.com and mobile apps.

You will focus on search, recommendations, and product data enrichment, collaborating across Ecommerce, Engineering, Analytics and Marketing. You will own models end-to-end from problem definition to production monitoring, apply NLP, embeddings and LLM-based methods, and drive measurable

Qualifications

• Bachelor's degree in Computer Science or related field; Master’s/PhD a plus.
• 5+ years of experience in data science or ML, or equivalent education/experience.
• Experience building ML for live, customer-facing digital products such as search, recommendations, ranking, personalization, or ads in ecommerce.
• Track record of taking models into production and owning them after launch, including monitoring, troubleshooting, and retraining.
• Expert proficiency in Python and SQL with production-quality coding practices.
• Hands-on experience with ML lifecycle practices and tooling (training pipelines, deployment, monitoring).
• Experience with NLP, embeddings, and large language models, including retrieval and reranking.

Responsibilities

• Build and improve ML models for product search and discovery.
• Develop personalization and recommendation models across site and app.
• Apply LLMs and computer vision to enrich product attributes at catalog scale.
• Partner with the Lead Data Scientist on conversational shopping and AI-assisted guidance.
• Build durable ML assets that carry across eCommerce platforms and vendor changes.

Skills

Python

SQL

NLP

A/B testing

ML lifecycle

Model deployment

Communication

Education

Bachelor's degree in Computer Science or related

Master's or PhD a plus

Tools

Databricks on Azure

PySpark

MLflow

GitHub Copilot

CI/CD for ML

Model registry

Feature stores

Retrieval-Augmented Generation

Job description

Purpose:

This position is responsible for building, deploying, and maintaining the ML models that power Floor & Decor's digital customer experience across flooranddecor.com, FND apps, and omni-channel digital shopping tools. Areas of work will include product search and discovery, personalization and recommendations, product data enrichment, and AI-assisted shopping. Working cross-functionally with Ecommerce Product, Engineering & Analytics, Data Engineering, Merchandising, and Digital Marketing, this role owns models end to end, from problem definition through production deployment, monitoring, and continuous improvement. In partnership with the Lead Data Scientist, AI/ML, this role helps set the standard for how digital machine learning is built and measured, with success defined by measurable improvement in how customers find, evaluate, and choose products.

Minimum Eligibility Requirements:

Bachelor's degree in Computer Science, Statistics, Mathematics, Engineering, Economics, or a related quantitative field (Master's or PhD 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. 5+ years of experience in data science or machine learning, 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 eCommerce, marketplaces, media, or similar environments. Track record of taking multiple models into production and owning them after launch, including monitoring, troubleshooting, and retraining. Depth in search and ranking or in recommendations and personalization, with the breadth to contribute across both. Applied experience with natural language processing, embeddings, and large language models, including retrieval and reranking, and evaluating their output. Expert proficiency in Python and SQL, with production-quality coding practices. Hands-on experience with ML lifecycle practices and tooling, including automated training and scoring pipelines, model deployment, and monitoring. Rigor in model evaluation and online experimentation (A/B testing) on digital products. Proficiency with AI-assisted development tools (e.g., Claude Code, GitHub Copilot, Cursor) to accelerate exploration, modeling, and engineering. Comfortable working while the roadmap and platforms take shape: frames ambiguous problems, starts simple, and adds complexity only when it pays off. Strong communication skills, with the ability to explain model behavior, tradeoffs, and expected impact to product, engineering, and business leaders.

Preferred Qualifications:

Experience helping build an emerging ML function: establishing the practices, tooling, and production patterns a growing team builds on. Databricks on Azure, PySpark, and CI/CD for ML, including MLflow, model registry, model serving, and feature stores. Experience extending, evaluating, or migrating commercial search or personalization platforms (e.g., Bloomreach, Constructor, Algolia, Dynamic Yield, Salesforce Personalization, Salesforce Commerce Cloud). Semantic search, retrieval-augmented generation (RAG), or computer vision applied to product discovery. eCommerce or omnichannel retail experience, ideally in high-consideration, project-based categories (home improvement, furniture, home décor). Experience with LLM-based agents or tool-using workflows. Experience with clickstream and customer behavioral data at scale. Experience with causal inference methods for measuring impact. Experience mentoring other data scientists or serving as technical lead on a multi-person ML effort.

Essential Job Functions:

Build and improve ML models for product search and discovery, using Floor & Decor's proprietary signals (customer behavior, store-level availability, local demand) to help customers find the right products faster. Develop personalization and recommendation models across site and app, including 'complete the project' recommendations and session-based models for anonymous and infrequent shoppers. Apply LLMs and computer vision to enrich product attributes at catalog scale, improving search, filtering, recommendations, and product feeds, with quality controls appropriate for technical product specifications. Partner with the Lead Data Scientist, AI/ML on conversational shopping and AI-assisted project guidance, owning retrieval quality and evaluation, including offline evaluation sets, and monitoring of quality, cost, latency, and safety. Build durable ML assets (enriched product data, embeddings, evaluation sets) that carry across eCommerce platform and vendor changes.