Principal, Data Scientist

Walmart•Bentonville, AR

7d•Onsite

About The Position

The Principal Data Scientist analyzes complex business problems within the data science discipline, challenges assumptions, identifies root causes, and recommends technology-focused solutions. The role defines deliverables and success metrics, quantifies business impact, evaluates business cases, translates business requirements into strategies and projects, and communicates return on investment. It also mentors team members and engages with the external community to learn about industry practices and build Walmart’s brand. The position identifies fit-for-purpose data sources, considers business requirements and service-level agreements, performs initial data-quality checks, and reviews junior associates’ work. It selects and develops analytical approaches for large, complex datasets; conducts exploratory data analysis; creates experiments and test-and-learn frameworks; interprets trends; and develops and improves machine-learning models using current methods in machine learning and artificial intelligence. The role requires implementing scalable data science solutions across cloud environments and using statistical, programming, machine-learning, big-data, data-integration, visualization, optimization, NLP, and deployment technologies. The job is located at 811 Excellence Dr, Bentonville, AR 72716.

Requirements

Master’s degree or equivalent in Statistics, Economics, Analytics, Mathematics, Computer Science, Information Technology, or a related field and 3 years of experience in analytics or related experience; alternatively, a bachelor’s degree or equivalent in one of those fields and 5 years of experience in analytics or related experience.
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 R statistical software.
Experience with machine-learning and deep-learning frameworks, including Scikit-learn, TensorFlow, PyTorch, and Keras.
Experience with computer vision tools and methods, including ResNet50 and fast.ai for image segmentation and object detection.
Experience with big-data technologies including BigQuery, Spark, Hive, and Hadoop, and data-integration technologies including Databricks, Snowflake, and Collibra.
Experience with feature engineering, extraction, and reduction using Python and R.
Experience conducting exploratory data analysis with data warehouses using SQL, setting up ETL jobs with Glue, Knime, and Alteryx, and visualizing data with Python, Tableau, Power BI, Matplotlib, Seaborn, ggplot, and 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, using Python and R.
Experience with operational research solutions and optimization libraries such as PuLP and CPLEX, 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 with 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.
The employer will accept any amount of experience with the required skills.

Responsibilities

Analyze business problems, question assumptions, and help identify root causes.
Recommend technology-focused approaches to resolve business problems and define deliverables, success criteria, and key metrics.
Quantify business impact and evaluate proposed business cases and initiatives.
Translate business requirements into strategies, initiatives, and projects aligned with business objectives, and drive execution of deliverables.
Build and articulate business cases and return on investment, and deliver work with demonstrable value.
Challenge business assumptions related to the role’s domain expertise.
Mentor team members on business insights and related developments, and review junior associates’ deliverables concerning data sources and data quality.
Engage proactively with the external community to build Walmart’s brand and learn industry practices.
Determine data requirements and identify suitable, fit-for-purpose data sources, including external sources where required.
Perform initial data-quality checks on extracted data and understand requirements’ priority order and service-level agreements.
Select modeling techniques for complex problems involving large-scale structured and unstructured data.
Iteratively select and develop variables and features in collaboration with the business, based on model responses.
Conduct exploratory data analysis, identify dimensions, design experiments, and create test-and-learn frameworks.
Interpret data to identify trends across future datasets.
Create continuous online model learning and iteratively enhance models.
Develop techniques using current machine-learning and artificial-intelligence trends to train algorithms for new datasets.
Implement scalable, production-grade data science solutions and generate actionable insights.

Benefits

Medical, vision, and dental coverage.
401(k).
Stock purchase plan.
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.
Annual or quarterly performance incentives.