Principal Data Scientist

Walmart Walmart · Retail · San Bruno, CA

Principal Data Scientist at Walmart focused on developing and deploying machine learning models for consumer-facing applications. The role involves end-to-end project lifecycle, from data analysis and feature engineering to model development, deployment, monitoring, and lifecycle management. Requires experience with various modeling techniques, experimental design, SQL, Python, and data visualization tools.

What you'd actually do

  1. Write code to develop the required solution and application features by determining the appropriate programming language and leveraging business, technical, and data requirements.
  2. Create test cases to review and validate the proposed solution design.
  3. Create proofs of concept.
  4. Test the code using the appropriate testing approach.
  5. Deploy software to production servers.

Skills

Required

  • media attribution models
  • MMM
  • A/B Tests
  • Causal Inference
  • Difference in Difference
  • SQL
  • Hadoop
  • BigQuery
  • Dataproc
  • Python
  • Tableau
  • PowerBI
  • Adobe Analytics
  • Linear regression
  • Logistic Regression
  • Random Forest
  • XGBoost
  • Github
  • Confluence
  • SharePoint

What the JD emphasized

  • Experience leading the development and deployment of media attribution models, including MMM.
  • Experience designing, executing, and measuring experimentational projects using A/B Tests, Causal Inference, and Difference in Difference.
  • Experience writing optimized SQL queries in Hadoop, BigQuery, and Dataproc platforms for data mining, ETL processes, and the synthesis of large structured and unstructured data.
  • Experience writing data pipelines and executing data transformations, statistical testing, experimentational techniques, mathematical and analytical model development in programming languages, including Python.
  • Experience creating data visualization using business intelligence tools, including Tableau, PowerBI, and Adobe Analytics.
  • Experience building classification machine learning algorithms, including Linear regression, Logistic Regression using Random Forest, and XGBoost.
  • Experience providing recommendations using complex analytics to executive audiences.
  • Experience modularizing a problem statement, developing a timelines plan, and working with multiple teams to achieve the same.
  • Experience collaborating with multiple teams across functions, defining RACI metrics, and leading end-to-end projects.
  • Experience documenting codes, methodologies, and projects using Github, Confluence, and SharePoint.

Other signals

  • Develop newer techniques by leveraging the latest trends in machine learning, artificial intelligence to train algorithms to apply models to new data sets.
  • Deploy models or model ensemble and ensure sustainability and maintenance overtime.
  • Implement model monitoring and model life-cycle management practices.