(ind) Senior, Data Analyst

Walmart · Retail · Bangalore, KA, India

Senior Data Analyst role focused on applying advanced analytics and machine learning to derive insights, build causal frameworks, and predict outcomes for Walmart's Marketplace platform. Responsibilities include data manipulation, model development, evaluation, and communication of findings to business and product teams. Requires expertise in SQL, Python, ML libraries, and visualization tools.

What you'd actually do

  1. You will be responsible for translating ambiguous business problems into actionable and data-driven solutions using advanced analytical techniques and storytelling
  2. Use advanced analytics and machine learning to derive insights, make causal frameworks and predict outcomes
  3. Leverage Tableau/Looker to share key metrics and communicate findings
  4. Define and track key metrics for your area, do RCAs, propose and test hypotheses
  5. Communicate findings and results effectively to partner teams

Skills

Required

  • SQL
  • Python
  • Tableau
  • Looker
  • Machine Learning
  • feature engineering
  • supervised machine learning models
  • model evaluation metrics
  • model validation techniques
  • unsupervised techniques

Nice to have

  • Masters degree
  • retail
  • eCommerce
  • consumer internet
  • Hive
  • Spark
  • Power BI
  • building analytical products
  • predictive models

What the JD emphasized

  • Hands-on with Machine Learning
  • Hands-on experience with supervised machine learning models
  • Ability to run, debug, and retrain existing machine learning models with new data independently
  • Hands-on exposure to unsupervised techniques and their application in exploratory or anomaly detection contexts

Other signals

  • Use advanced analytics and machine learning to derive insights, make causal frameworks and predict outcomes
  • Hands-on with Machine Learning
  • Strong proficiency in feature engineering
  • Hands-on experience with supervised machine learning models
  • Ability to select appropriate modeling approaches
  • Understanding and application of model evaluation metrics
  • Experience with model validation techniques
  • Ability to run, debug, and retrain existing machine learning models
  • Experience in supporting and contributing to feature pipelines
  • Ability to validate model outputs
  • Hands-on exposure to unsupervised techniques
  • Ability to perform structured exploratory analysis
  • Experience working with large datasets and scalable data environments
  • Ability to translate model outputs into actionable business insights