Principal, Data Scientist

Walmart•Bentonville, AR

6d•Onsite

About The Position

The Principal Data Scientist analyzes complex business problems using analytics, big-data analytics, and automation methods; 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 articulates return on investment. It also challenges assumptions, mentors team members, and engages with the external community to build Walmart’s brand and learn industry practices. The position identifies fit-for-purpose data sources, understands data requirements and service-level agreements, and performs initial data-quality checks. It selects and develops modeling techniques for large, complex structured and unstructured datasets; conducts exploratory data analysis; designs experiments and test-and-learn frameworks; interprets trends; and develops continuously learning models and new machine-learning and artificial-intelligence techniques. The role applies data-science solutions across cloud environments and uses programming, statistical, machine-learning, computer-vision, big-data, data-integration, visualization, optimization, natural-language-processing, and model-deployment technologies. It translates business problems into scalable, production-grade data-science solutions that generate actionable insights. The position 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, plus 3 years of experience in analytics or related experience; OR a bachelor’s degree or equivalent in one of those fields, plus 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 statistical software such as R.
Experience with machine-learning and deep-learning frameworks including Scikit-learn, TensorFlow, PyTorch, and Keras.
Experience with computer vision, 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 techniques using Python and R.
Experience conducting exploratory data analysis with data warehouses using SQL and setting up ETL jobs with Glue, Knime, or Alteryx.
Experience with data visualization using Python, Tableau, PowerBI, 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 optimization solutions using libraries such as PuLP and CPlex, optimization, and neural networks.
Experience with natural-language processing using tools such as 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 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 within the discipline, question assumptions, and help identify root causes.
Recommend approaches to resolve business problems with technology-focused solutions.
Set deliverables against success criteria, define metrics to measure progress and effectiveness, and quantify business impact.
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 in areas of domain expertise, mentor team members on business insights and developments, and engage with the external community.
Identify suitable data sources for requirements, including external sources where needed, and perform initial data-quality checks.
Review junior associates’ deliverables and guide them on data sources and data quality.
Select modeling techniques for complex problems involving large structured and unstructured datasets.
Develop variables and features iteratively with business partners and conduct exploratory data analysis.
Identify experiment 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 machine-learning and artificial-intelligence trends to train algorithms for new datasets.
Implement and deploy scalable, production-grade data-science solutions that 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.
Performance-based incentive awards, including annual or quarterly performance incentives.