Director, Data Analytics

Walmart•Bentonville, AR

17h•Onsite

About The Position

Lead Walmart’s analytics practice supporting audit teams by turning data, automation, and emerging technology into products that improve audit coverage, surface risk sooner, and deliver measurable business results. Partner with audit teams, business partners, and technology functions to identify problems, develop and scale solutions, and promote responsible use of data. Lead an analytics team and foster innovation, collaboration, and continuous learning. Own an analytics product roadmap aligned with audit priorities. Define customer needs and desired outcomes, prioritize work, assess costs and resources, deliver solutions incrementally, and track adoption, quality, time savings, and risk insights. Lead governance and enterprise compliance processes for analytics, AI, and automation solutions, coordinating security, privacy, compliance, and technology reviews and approvals. Identify and assess data across business systems and distributed data stores; perform quality checks and exploratory analysis; apply statistical methods and models; and communicate findings through effective visualizations and compelling narratives. Advance data strategy, governance, data quality, access practices, and requirements for privacy, security, storage, retention, and documentation. Recruit, develop, and mentor talent, address capability gaps, support succession planning, and build a curious team grounded in Walmart’s standards of integrity and ethics.

Requirements

Bachelor’s degree in Business, Engineering, Statistics, Economics, Analytics, Mathematics, Arts, Finance, or a related field and 5 years of experience in data analysis, data science, statistics, or a related field; or a master’s degree in a listed or related field and 3 years of such experience; or 7 years of such experience.
2 years of supervisory experience.
Experience leading innovation and delivering solutions with a product mindset, including defining success measures and reporting measurable impact and business value.
Strong analytical background, including exploratory data analysis and statistical methods such as hypothesis testing, confidence intervals, correlation, and statistical modeling.
Experience using analytical tools or languages such as SQL, Python, SAS, Oracle, Apache Mahout, or Excel.
Experience identifying and working with data across business systems and distributed data stores, including SQL and NoSQL technologies; understanding of data modeling, integration, storage, and warehousing.
Experience creating visualizations and communicating insights with tools such as Tableau, Power BI, Python, or R, including libraries such as Matplotlib, Plotly, or ggplot; ability to tell a compelling story to audiences from analysts to executives.
Leadership experience engaging peers and stakeholders across teams, driving change, and developing others through mentoring, coaching, and teaching.
Ability to adapt as priorities, technology, and risks change, challenge assumptions constructively, and keep teams focused on delivery.
Knowledge of data management and governance, including data quality frameworks, profiling and cleansing tools, access practices, and privacy, security, and retention requirements.
Knowledge of business practices and metrics across functions such as product, finance, technology, business systems, or audit.

Nice To Haves

Experience in data science, data analysis, statistics, or a related field.
PhD in Machine Learning, Computer Science, Information Technology, Operations Research, Statistics, Applied Mathematics, Econometrics, or a related field.
Related industry experience, such as retail, merchandising, healthcare, or eCommerce.
Successful completion of assessments in data analysis and Business Intelligence tools and scripting languages, such as SQL, Python, Spark, Scala, R, Power BI, or Tableau.
Supervisory experience.

Responsibilities

Identify opportunities to apply analytics, automation, and emerging technology to audit planning, risk assessment, testing, and reporting; prototype solutions and define success criteria, metrics, and measurable return on investment.
Partner with audit teams to understand their work, co-design useful solutions, lead adoption, influence stakeholders, and scale successful changes.
Share tools, methods, and lessons through playbooks, demonstrations, communities of practice, mentoring, coaching, and teaching.
Own an analytics product roadmap aligned with audit priorities; define customers, problems, and outcomes; prioritize work; assess costs and resources; deliver incrementally; and report on adoption, quality, time saved, and risk insights.
Ensure analytics products, AI solutions, and automation initiatives comply with enterprise technology, security, and governance requirements.
Coordinate internal reviews and approvals, including Solution Security Plan (SSP), Enterprise Privacy Risk Assessment (EPRA), and other applicable reviews; partner with Information Security, Privacy, Compliance, and Technology teams to identify and address risks and obtain approvals before deployment.
Identify fit-for-purpose data across systems and distributed data stores, conduct data quality checks and exploratory analysis, apply statistical methods and models, and surface insights.
Select appropriate visualization tools and communicate findings to analysts, audit leaders, and executives; partner with user experience designers and engineers when front-end applications are needed.
Apply and influence data strategy across audit and business domains; define data quality rules, metrics, and service-level strategies; and recommend improvements to data governance policies and practices.
Manage user access as needed and support data privacy, security, storage, retention, and documentation requirements.
Recruit, develop, and mentor talent; identify capability gaps; support succession planning and continuous learning; and foster an adaptable team culture grounded in integrity and ethics.