Sr Data Scientist, Online Experimentation

With a career at The Home Depot, you can be yourself and also be part of something bigger.

Position Purpose:

The Sr. Data Scientist, Experimentation Science owns the statistical foundation of how The Home Depot learns from its online customers. Every year the Online organization runs experiments that decide what ships, what gets rebuilt and what gets abandoned. This role makes those decisions trustworthy. You own the inference methods behind the results, the diagnostics that catch a broken test before anyone reads it, and the design guidance that gets product teams to a well-powered, well-instrumented experiment before a single customer is exposed. 

The work happens at two altitudes. At the methods altitude, you define and validate the statistical approach used across online experimentation: estimator choice, variance reduction, sequential testing, heterogeneous treatment effects, and causal inference where randomization is not possible. At the team altitude, you sit with product managers, engineers, designers and merchants to shape hypotheses, size effects honestly, and interpret results without overclaiming. You are the person who says the test cannot answer that question at this traffic, and the person who says this result is real and we should act on it. 

This role also builds the AI layer that scales the practice. Experimentation is full of high-volume, judgment-adjacent work: design review, instrumentation checks, readout drafting, anomaly triage. You will design, build and evaluate LLM-based and agentic workflows that absorb that work, so the team's scientists spend their time on the questions that need a human. That means writing the tooling, defining how it connects to our data and platforms, and holding it to an evaluation standard as rigorous as the one you apply to experiments. 

Success is measured by decision quality and experiment velocity together: more tests, faster, that leaders actually believe. This is a senior individual contributor role with broad technical influence, setting the standards that other data scientists and analysts across the Online organization follow. 

Key Responsibilities:

• 35% Solution Development - Proficiently design and develop algorithms and models to use against large datasets to create business insights; Execute tasks with high levels of efficiency and quality; Make appropriate selection, utilization and interpretation of advanced analytical methodologies; Effectively communicate insights and recommendations to both technical and non-technical leaders and business customers/partners; Prepare reports, updates and/or presentations related to progress made on a project or solution; Clearly communicate impacts of recommendations to drive alignment and appropriate implementation
• 30% Project Management & Team Support - Work with project teams and business partners to determine project goals; Provide direction on prioritization of work and ensure quality of work; Provide mentoring and coaching to more junior roles to support their technical competencies; Collaborate with managers and team in the distribution of workload and resources; Support recruiting and hiring efforts for the team
• 20% Business Collaboration - Leverage extensive business knowledge into solution approach; Effectively develop trust and collaboration with internal customers and cross-functional teams; Provide general education on advanced analytics to technical and non-technical business partners; Deep understanding of IT needs for the team to be successful in tackling business problems; Actively seek out new business opportunities to leverage data science as a competitive advantage
• 15% Technical Exploration & Development - Seek further knowledge on key developments within data science, technical skill sets, and additional data sources; Participate in the continuous improvement of data science and analytics by developing replicable solutions (for example, codified data products, project documentation, process flowcharts) to ensure solutions are leveraged for future projects; Define best practices and develop clear vision for data analysis and model productionalization; Contribute to library of reusable algorithms for future use, ensuring developed codes are documented

Direct Manager/Direct Reports:

• This position reports to manager or above
• This position has 0 Direct Reports

Travel Requirements:

• Typically requires overnight travel less than 10% of the time.

Physical Requirements:

• Most of the time is spent sitting in a comfortable position and there is frequent opportunity to move about. On rare occasions there may be a need to move or lift light articles.

Working Conditions:

• Located in a comfortable indoor area. Any unpleasant conditions would be infrequent and not objectionable.

Minimum Qualifications:

• Must be eighteen years of age or older.
• Must be legally permitted to work in the United States.

Preferred Qualifications:

• Advanced degree (MS or PhD) in Statistics, Economics, Biostatistics, Computer Science, Applied Mathematics or a related quantitative field 

• 6+ years of quantitative analytics experience, including hands-on ownership of online controlled experiments end to end 

• Deep expertise in experimental design, hypothesis testing, power analysis and causal inference 

• Working knowledge of variance reduction, sequential and always-valid testing, and heterogeneous treatment effect estimation 

• Experience with quasi-experimental methods where randomization is not possible, such as difference-in-differences, synthetic control or geo-based design 

• Proficiency in Python or R for statistical analysis, and in SQL against a cloud warehouse (preferably Google BigQuery) 

• Experience building production or near-production workflows with LLMs, including prompt design, tool and function calling, retrieval, and multi-step or agentic orchestration 

• Experience defining evaluation for AI systems, including offline benchmarks, human review loops and monitoring for regression 

• Experience with modern experimentation platforms, including server-side testing and feature flag patterns 

• Experience building or independently validating an experimentation statistics engine, not only consuming one 

• Familiarity with e-commerce or retail metrics, including conversion, average order value, attach rate and lifetime value 

• Demonstrated ability to explain statistical trade-offs and uncertainty to non-technical partners and senior leaders 

• Experience mentoring analysts or data scientists and setting technical standards without formal authority 

• Comfort with version control, collaborative code review and shipping internal tooling others depend on

Minimum Education:

• The knowledge, skills and abilities typically acquired through the completion of a bachelor's degree program or equivalent degree in a field of study related to the job.

Preferred Education:

• No additional education

Minimum Years of Work Experience:

• 5

Preferred Years of Work Experience:

• No additional years of experience

Minimum Leadership Experience:

• None

Preferred Leadership Experience:

• None

Certifications:

• None

Competencies:

• Attracts Top Talent: Attracting and selecting the best talent to meet current and future business needs
• Business Insight: Applying knowledge of the business and the marketplace to advance the organization's goals
• Collaborates: Building partnerships and working collaboratively with others to meet shared objectives
• Communicates Effectively: Developing and delivering multi-mode communications that convey a clear understanding of the unique needs of different audiences
• Cultivates Innovation: Creating new and better ways for the organization to be successful
• Customer Focus: Building strong customer relationships and delivering customer-centric solutions
• Develops Talent: Developing people to meet both their career goals and the organization's goals
• Directs Work: Provides direction, delegating and removing obstacles to get work done
• Drives Results: Consistently achieving results, even under tough circumstances
• Nimble Learning: Actively learning through experimentation when tackling new problems, using both successes and failures as learning fodder
• Optimizes Work Processes: Knowing the most efficient and effective processes to get things done, with a focus on continuous improvement
• Self-Development: Actively seeking new ways to grow and be challenged using both formal and informal development channels