Senior Data Scientist

Walmart•Bentonville, AR

6d•Onsite

About The Position

The Senior Data Scientist identifies suitable data sources, checks the quality of extracted data, and applies defined data strategies to routine business problems. The role develops and tests code, supports deployment and scaling of analytical models, and evaluates model accuracy, fit, validity, and robustness using appropriate metrics and testing techniques. The position conducts exploratory data analysis, selects and develops analytical models, defines and refines model features, and designs and conducts experiments. It performs trend and cluster analysis to address business problems and provides recommendations and insights. The Senior Data Scientist translates business requirements into data science solutions, builds business cases, and connects business needs with tangible results. The role also creates data visualizations and dashboards, communicates findings to business and executive audiences, and works with user experience and user interface teams as needed. It guides and mentors junior associates in analytics, modeling, and communication techniques. The position is based at 811 Excellence Dr, Bentonville, AR 72716. The stated pay range is $110,302 to $180,000 per year, with additional annual or quarterly performance incentives. Walmart offers benefits subject to eligibility and plan terms.

Requirements

Master’s degree or equivalent in Statistics, Economics, Analytics, Mathematics, Computer Science, Information Technology, or a related field, plus 1 year of experience in an analytics-related field; OR bachelor’s degree or equivalent in one of these fields, or a related field, plus 3 years of experience in an analytics-related field.
Experience with SQL and big-data tooling to mine and transform high-volume logistics datasets.
Experience using Python for production-grade data science.
Experience with time-series forecasting.
Experience with MLOps and productionization, including feature pipelines, model monitoring, Airflow-style orchestration, and reliability.
Experience with experimentation and causal inference, including A/B tests and uplift modeling, to translate business needs into measurable data science solutions.
Experience applying supervised or unsupervised machine learning to improve efficiency, cost, and experience.
Experience developing and testing large-scale algorithms, including design, A/B evaluation, and KPI alignment.
Experience with cross-functional communication and stakeholder management involving product, engineering, and operations.
Experience with Tableau dashboarding and BI visualization, including executive dashboards, operational reporting, and self-service analytics.
Experience with business intelligence, including data warehousing, ETL, KPI frameworks, and integration with tools such as Tableau.
Experience with executive presentations and storytelling.

Responsibilities

Identify suitable data sources based on requirements and service-level agreements, and perform initial data quality checks on extracted data.
Apply defined data strategy principles to routine business problems involving a single function.
Support analytical model evaluation, production deployment, scalability, and sustainability.
Develop and test code using recommended programming languages and testing approaches, based on business, technical, and data requirements.
Identify model evaluation metrics and apply best-practice testing and tuning techniques to assess model accuracy, fit, validity, and robustness.
Generate graphical representations of data and model outcomes based on customer requirements, and work with UX designers and UI engineers as required.
Present findings and influence team and business audiences using suitable data visualization frameworks and stakeholder-appropriate communication.
Guide and mentor junior associates on data storytelling, modeling, and analytics techniques.
Recommend solutions to complex business issues, develop project business cases with projected ROI or cost savings, and translate business requirements into aligned projects and tasks.
Connect business needs with practical solutions and identify relevant business insights.
Translate or co-own business problems within the discipline as data-related or mathematical problems and identify appropriate methods and tools.
Select and develop analytical models suited to structured, complex data; conduct exploratory data analysis, including statistical analysis, hypothesis testing, and statistical inference.
Define and finalize model features, and introduce or revise features to improve analysis and outcomes.
Define experiment dimensions, finalize experiment designs, test hypotheses, and conduct experiments.
Perform trend and cluster analysis to address business problems and provide recommendations and insights.
Develop and test large-scale algorithms, including design, A/B evaluation, and KPI alignment.
Build executive dashboards, operational reporting, and self-service analytics, and support BI integration and KPI frameworks.