(usa)staff, Data Scientist

Walmart · Retail · Sunnyvale, CA

Staff Data Scientist role focused on designing and deploying advanced forecasting models using statistical, ML, and deep learning methods. The role emphasizes explainability (XAI), graph-based modeling, and building agentic workflows for forecasting. Requires strong Python skills, experience with deep learning frameworks, and production-grade ML model delivery.

What you'd actually do

  1. Design and deploy statistically and ML models to address high-impact financial forecasting needs, ensuring alignment with Walmart’s business objectives.
  2. Perform statistical analysis across large data sets and within defined segments to empower data driven decisions.
  3. Own E2E forecasting lifecycle, including scoping, feature engineering, model development, experimentation, monitoring and ongoing performance optimizations.
  4. Develop advanced time series solutions using: Statistical methods (ETS, ARIMA/SARIMA, State Space Models), ML approaches (GBMs, Random Forests, linear/elastic models with engineered time features), Deep learning (RNN/LSTM/GRU, Temporal Convolutional Networks (TCNs), TimesFM), Probabilistic forecasting and uncertainty quantification (quantile regression, Bayesian approaches, conformal prediction, prediction intervals).
  5. Build explainable forecasting systems: model interpretability, feature attribution, drivers of change, scenario analysis, and stakeholder-facing narratives.

Skills

Required

  • Python
  • SQL
  • PyTorch
  • TensorFlow
  • scikit-learn
  • XGBoost
  • LightGBM
  • statsmodels
  • Prophet-like tools
  • SHAP
  • Integrated Gradients
  • permutation importance
  • counterfactuals

Nice to have

  • Spark
  • Databricks
  • Airflow
  • Kubernetes
  • MLOps
  • AgentOps
  • PyG
  • DGL
  • causal inference
  • decision-focused forecasting

What the JD emphasized

  • deep hands-on exposure to forecasting and predictive modeling
  • production grade ML models
  • explainable AI methods
  • Agentic workflows

Other signals

  • design and deploy advanced forecasting models
  • build and deploy state-of-the-art time series models
  • drive explainability and trust (XAI)
  • explore next-generation approaches such as graph neural networks
  • Build Agentic workflows to enable chat based forecasting explainability and scenario planning