Senior Data Scientist

Walmart • Bentonville, AR

6d • $90,000 - $180,000 • Onsite

About The Position

The Senior Data Scientist applies statistical, analytical, and machine learning methods to structured and complex datasets to solve business problems and deliver actionable insights. The role includes selecting and developing analytical models; exploring data; defining and refining model features; designing and analyzing experiments; and evaluating model fit, accuracy, validity, and robustness. It also supports production deployment, operational readiness, scalability, and long-term maintenance of analytical solutions, and develops solutions and application features using programming languages and business and domain requirements.

The role applies methods including graphical models, Bayesian inference, natural language processing, computer vision, neural networks, support vector machines, random forests, regression, and optimization. It uses programming and analytical tools including Python, R, SQL, Java, C++, PySpark, Excel, Seaborn, MATLAB plot, PyTorch, TensorFlow, and Keras. Testing methodologies include static and dynamic testing, software composition analysis, and manual penetration testing.

The minimum education requirement is a master's degree or equivalent in Computer Science, Statistics, Engineering, or a related field plus one year of experience in analytics or a related field; alternatively, a bachelor's degree or equivalent in one of those fields plus three years of such experience. The employer will accept any amount of graduate coursework, graduate research experience, or professional experience with the required skills.

Requirements

• Master’s degree or equivalent in Computer Science, Statistics, Engineering (any), or a related field plus one year of experience in analytics or a related field; or a bachelor’s degree or equivalent in one of those fields plus three years of experience in analytics or a related field.
• Experience coding in object-oriented programming languages, including Python.
• Experience applying probability, hypothesis testing, and regression analysis.
• Experience applying supervised, unsupervised, and semi-supervised machine learning, including linear and logistic regression, decision trees, support vector machines, and deep learning models using PyTorch, TensorFlow, and Keras.
• Experience building optimization models and solving them using integer programming or linear programming.
• Experience creating data visualizations using Seaborn and MATLAB plot.
• Experience writing SQL queries.
• Experience working with large datasets using languages such as PySpark.
• Experience running scientific experiments and analyzing experiment results.
• Knowledge of feature relevance and selection, exploratory data analysis, advanced statistical methods, and modeling approaches.
• Knowledge of multivariate calculus, statistical foundations of machine learning models, advanced Excel techniques, and classical and numerical optimization methods, including Newton-Raphson, gradient descent, linear programming, integer programming, and quadratic programming.
• Experience or knowledge of model fit testing, tuning, and validation, including metrics such as chi-square tests, ROC curves, and root mean square error.
• Experience with programming languages including R, Java, C++, and Python.
• The employer will accept any amount of graduate coursework, graduate research experience, or professional experience with the required skills.

Responsibilities

• Select appropriate analytical modeling techniques for structured and complex datasets and develop customized analytical models.
• Conduct exploratory data analysis, statistical analysis, hypothesis testing, and statistical inference on available data.
• Define and refine model features based on performance, introducing new or revised features to improve analytical outcomes.
• Design and execute experiments by identifying key dimensions, finalizing experimental design, and testing hypotheses.
• Perform trend and cluster analyses to address business problems and provide actionable insights and recommendations.
• Test, tune, and validate models using appropriate methods and metrics, including chi-square tests, ROC curves, and root mean square error.
• Identify evaluation metrics appropriate to model objectives and business requirements.
• Assess the impact of variables and features on model performance and evaluate accuracy, fit, validity, and robustness across multi-stage models and model ensembles.
• Use knowledge of model performance drivers, server environments, and model storage formats to support production deployment.
• Contribute to making analytical models and techniques production-ready and operationally sustainable; support model evaluation, scalability, and long-term maintenance.
• Develop required solutions and application features using programming languages, business requirements, and domain knowledge.
• Apply testing methodologies, including static testing, dynamic testing, software composition analysis, and manual penetration testing, to assess code quality, functionality, and security.

Benefits

• Competitive pay and performance-based incentive awards, including annual or quarterly performance incentives.
• Medical, vision, and dental coverage.
• 401(k), stock purchase, and company-paid life insurance.
• Paid time off, including sick leave, parental leave, family care leave, bereavement leave, jury duty leave, and voting leave.
• Short-term and long-term disability.
• Education assistance, including company-paid college degrees.
• Company discounts.
• Military service pay.
• Adoption expense reimbursement.

What This Job Offers

• Job Type: Full-time
• Career Level: Senior