Principal, Data Scientist

Walmart•Bentonville, AR

7d•$144,040 - $220,000•Onsite

About The Position

The Principal, Data Scientist analyzes complex business problems and develops technology-focused solutions using analytics, big data, and automation. The role evaluates business context, requirements, use cases, and assumptions; defines deliverables and metrics; quantifies business impact; and translates business needs into strategies and projects aligned with business objectives. It also builds business cases and demonstrates return on investment, mentors team members, and engages with the external community to learn industry practices and support Walmart’s brand. The position identifies fit-for-purpose data sources across business domains and systems, accounts for data requirements and service-level agreements, conducts initial data-quality checks, and guides junior associates on data sourcing and quality. It selects and develops modeling techniques for complex problems involving large structured and unstructured datasets; performs exploratory data analysis; designs experiments and test-and-learn frameworks; interprets trends; and develops continuously learning models using current machine-learning and artificial-intelligence methods. The role applies statistical, machine-learning, deep-learning, computer-vision, optimization, natural-language-processing, and time-series forecasting techniques. It implements scalable data-science solutions in cloud environments and translates business problems into production-grade solutions that generate actionable insights.

Requirements

Master’s degree or equivalent in Statistics, Economics, Analytics, Mathematics, Computer Science, Information Technology, or a related field, plus 3 years of experience in analytics or a related field; OR a bachelor’s degree or equivalent in one of those fields, plus 5 years of experience in analytics or a related field.
Experience implementing data-science solutions in cloud environments, including Google Cloud and Azure.
Experience coding in object-oriented languages, including Python and C++, and using statistical software such as R.
Experience with machine-learning and deep-learning frameworks including scikit-learn, TensorFlow, PyTorch, and Keras.
Computer-vision experience with ResNet50 and fast.ai for image segmentation and object detection.
Experience with big-data technologies including BigQuery, Spark, Hive, and Hadoop, and data-integration tools including Databricks, Snowflake, and Collibra.
Experience with feature engineering, extraction, and reduction techniques using Python and R.
Experience performing exploratory data analysis in data warehouses using SQL and setting up ETL jobs with Glue, Knime, or Alteryx.
Experience with data visualization using Python, Tableau, Power BI, Matplotlib, Seaborn, ggplot, or Spotfire.
Experience implementing supervised and unsupervised machine-learning techniques, including K-means clustering, principal component analysis, hierarchical clustering, ARIMA, hierarchical linear modeling, and gradient boosting in Python or R.
Experience with operational research and optimization solutions using libraries such as PuLP and CPLEX, as well as optimization and neural networks.
Experience with natural-language processing tools and methods including gensim and BERT.
Experience deploying time-series forecasting solutions using machine-learning techniques such as DeepAR in Python and R.
Experience identifying model-evaluation metrics and applying appropriate testing and tuning methods to assess accuracy, fit, validity, and robustness using advanced statistics.
Experience translating business problems into production-grade, scalable data-science solutions using MLflow, Docker, and Kubernetes.

Responsibilities

Analyze business problems, question assumptions, identify root causes, and recommend technology-focused solutions.
Set deliverables and key metrics based on success criteria, measure solution effectiveness, and quantify business impact.
Evaluate business cases and translate requirements into strategies, initiatives, and projects aligned with business objectives.
Build and articulate business cases and return on investment, and deliver work with demonstrable value.
Challenge business assumptions within the area of expertise and mentor team members on business insights and developments.
Engage with the external community to learn about industry practices and build Walmart’s brand.
Identify suitable data sources for business requirements, including external sources when needed, and perform initial data-quality checks.
Review junior associates’ deliverables and provide guidance on data sources and quality.
Select modeling techniques for complex problems involving large-scale structured and unstructured data.
Select and iteratively develop variables and features in collaboration with the business.
Conduct exploratory data analysis, identify dimensions, design experiments, and create test-and-learn frameworks.
Interpret data to identify trends across future datasets and develop continuous online learning and iterative model enhancements.
Develop techniques using current machine-learning and artificial-intelligence approaches to apply models to new datasets.
Implement scalable, production-grade data-science solutions that generate actionable insights.