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Senior Data Scientist

Walmart

Sunnyvale, California, United StatesOn Site

Original Job Summary

Job Overview

The Senior Data Scientist at Walmart is responsible for developing analytical models, performing data quality assessments, and delivering data-driven business insights. The role includes mentoring junior associates and ensuring models can be deployed into production, while working closely with business stakeholders and UI/UX teams.

Duties & Responsibilities

  • Conduct exploratory data analysis and hypothesis testing
  • Develop custom analytical models and perform trend analysis
  • Mentor junior associates in modeling and analytics techniques
  • Write and test code using Python, Spark, TensorFlow, and Keras
  • Collaborate with UI/UX teams for front end applications
  • Deploy machine learning models using Docker containers and ML Flow

Skills & Technologies

  • Python, Spark, TensorFlow, Keras
  • Relational and non-relational databases
  • NLP techniques including Transformers and Bert Embeddings
  • Flask, Fast API, and Docker containers
  • ML Flow for model lifecycle management

Education & Experience

Master’s degree in a related field with 1 year of relevant experience OR Bachelor’s degree with 3 years of experience in analytics.

Benefits

Competitive pay with performance-based incentive awards, comprehensive health benefits, 401(k), stock purchase, PTO, and additional employee benefits.

Key skills/competency

Senior Data Scientist, Python, Spark, TensorFlow, Keras, NLP, ML Flow, Docker, Data Analysis, Mentoring

How to Get Hired at Walmart

🎯 Tips for Getting Hired

  • Research Walmart's culture: Study their values and recent news.
  • Customize your resume: Highlight ML and data analytics skills.
  • Prepare coding examples: Showcase Python and Spark projects.
  • Practice interview questions: Focus on analytical and leadership skills.

📝 Interview Preparation Advice

Technical Preparation

Review Python and Spark basics.
Practice TensorFlow, Keras model building.
Set up and test Docker containers.
Familiarize with ML Flow procedures.

Behavioral Questions

Explain previous mentoring experiences concisely.
Describe cross-team communication examples.
Discuss handling project challenges clearly.
Highlight leadership during complex projects.