Senior Data Scientist

Walmart•Bentonville, AR

11d•$90,000 - $180,000•Onsite

About The Position

The Senior Data Scientist will apply knowledge of feature relevance and selection, exploratory data analysis techniques, advanced statistical methods, and modeling approaches such as graphical models, Bayesian inference, basic natural language processing, computer vision, neural networks, support vector machines, and random forests. This role involves utilizing multivariate calculus, statistical foundations of machine learning models, advanced Excel techniques, and programming languages such as R and Python, along with classical and numerical optimization methods including Newton-Raphson, gradient descent, linear programming, integer programming, and quadratic programming. The Senior Data Scientist will select appropriate analytical modeling techniques for structured and complex datasets and develop customized analytical models. They will conduct exploratory data analysis, including statistical analysis, hypothesis testing, and statistical inference, on available data. This role also involves defining and refining features based on model performance, introducing new or revised features to improve analytical outcomes, and designing experiments by identifying key dimensions, finalizing experimental design, testing hypotheses, and executing analyses. The Senior Data Scientist will perform trend and cluster analyses to address business problems and deliver actionable insights and recommendations. They will apply knowledge of model fit testing, tuning, and validation techniques, including chi-square tests, ROC curves, root mean square error, and other relevant metrics, and identify appropriate evaluation metrics based on model objectives and business requirements. This position will assess the impact of variables and features on model performance and apply best-practice testing and tuning techniques to evaluate accuracy, fit, validity, and robustness across multi-stage models and model ensembles. The Senior Data Scientist will leverage understanding of model performance drivers, server environments, and model storage formats to support production deployment and contribute to efforts ensuring analytical models and techniques are production-ready and operationally sustainable. They will support model evaluation processes and enable scalability and long-term maintenance of analytical solutions. This role requires utilizing programming languages such as SQL, Java, C++, and Python, along with knowledge of business and domain requirements, to develop required solutions and application features. The Senior Data Scientist will apply appropriate testing methodologies, including static testing, dynamic testing, software composition analysis, and manual penetration testing, to ensure code quality, functionality, and security.

Requirements

Master’s degree or the equivalent in Computer Science, Statistics, Engineering (any), or related field plus 1 year of experience in analytics or a related field; OR Bachelor's degree or the equivalent in Computer Science, Statistics, Engineering (any), or related field plus 3 years of experience in analytics or a related field.
Experience with coding in object-oriented programming languages (Python).
Experience applying statistical techniques such as probability, hypothesis testing and regression analysis.
Experience applying machine learning techniques (supervised, unsupervised and semi-supervised models) such as linear and logistic regression, decision trees, support vector machines and deep learning models using PyTorch, TensorFlow and Keras.
Experience building optimization models.
Experience solving optimization models using Integer programming or linear programming.
Experience creating clear and informative visualizations of data to convey insights using Seaborn and MATLAB plot.
Experience writing SQL queries.
Experience working with large datasets with languages such as PySpark.
Experience running experiments scientifically.
Experience analyzing experiments results.

Responsibilities

Apply knowledge of feature relevance and selection, exploratory data analysis techniques, advanced statistical methods, and modeling approaches such as graphical models, Bayesian inference, basic natural language processing, computer vision, neural networks, support vector machines, and random forests.
Utilize multivariate calculus, statistical foundations of machine learning models, advanced Excel techniques, and programming languages such as R and Python, along with classical and numerical optimization methods including Newton-Raphson, gradient descent, linear programming, integer programming, and quadratic programming.
Select appropriate analytical modeling techniques for structured and complex datasets and develop customized analytical models.
Conduct exploratory data analysis, including statistical analysis, hypothesis testing, and statistical inference, on available data.
Define and refine features based on model performance, introducing new or revised features to improve analytical outcomes.
Design experiments by identifying key dimensions, finalizing experimental design, testing hypotheses, and executing analyses.
Perform trend and cluster analyses to address business problems and deliver actionable insights and recommendations.
Apply knowledge of model fit testing, tuning, and validation techniques, including chi-square tests, ROC curves, root mean square error, and other relevant metrics.
Identify appropriate evaluation metrics based on model objectives and business requirements.
Assess the impact of variables and features on model performance and apply best-practice testing and tuning techniques to evaluate accuracy, fit, validity, and robustness across multi-stage models and model ensembles.
Leverage understanding of model performance drivers, server environments, and model storage formats to support production deployment.
Contribute to efforts ensuring analytical models and techniques are production-ready and operationally sustainable.
Support model evaluation processes and enable scalability and long-term maintenance of analytical solutions.
Utilize programming languages such as SQL, Java, C++, and Python, along with knowledge of business and domain requirements, to develop required solutions and application features.
Apply appropriate testing methodologies, including static testing, dynamic testing, software composition analysis, and manual penetration testing, to ensure code quality, functionality, and security.