(USA)Staff, Data Scientist

About the Role

We’re looking for a Staff Data Scientist to design and build advanced forecasting models to ensure accurate financial planning and analysis (FP&A) that power critical business decisions. You’ll build and deploy state-of-the-art time series models (classical + ML + deep learning), drive explainability and trust (XAI), and explore next-generation approaches such as graph neural networks for spatiotemporal and relational forecasting problems. You’ll partner closely with engineering, product, and stakeholders to deliver measurable impact at scale.

What You’ll Do

• Design and deploy statistically and ML models to address high-impact financial forecasting needs, ensuring alignment with Walmart’s business objectives.
• Perform statistical analysis across large data sets and within defined segments to empower data-driven decisions.
• Own E2E forecasting lifecycle, including scoping, feature engineering, model development, experimentation, monitoring and ongoing performance optimizations.
• 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).
• Build explainable forecasting systems: model interpretability, feature attribution, drivers of change, scenario analysis, and stakeholder-facing narratives.
• Apply graph-based and spatiotemporal modeling where relationships matter: GNNs, temporal graphs, graph embeddings.
• Establish strong evaluation and monitoring: backtesting, leakage prevention, stability checks, drift detection, calibration of uncertainty, and post-deployment performance tracking.
• Drive best practices in MLOps and production readiness: reproducible pipelines, scalable training/inference, model versioning, and governance.
• Build Agentic workflows to enable chat based forecasting explainability and scenario planning.
• Collaborate with cross-functional partners including Product, Business, Data Science and Engineering.
• Mentor other data scientists, set modeling standards, and influence technical direction across teams.

What You’ll Bring (Required)

• 8+ years in data science / applied ML (or PhD + 5 years), with deep hands-on exposure to forecasting and predictive modeling.
• Demonstrated experience delivering production grade ML models with measurable business outcomes.
• Strong knowledge of time series topics: seasonality, hierarchies, intermittent demand, holidays/events, promotions, missingness, outliers, anomaly detection, and regime changes.
• Hands-on experience with deep learning frameworks (PyTorch or TensorFlow) and modern architectures for time series.
• Practical experience with explainable AI methods and communicating model reasoning to non-technical stakeholders.
• Excellent coding skills in Python; strong grasp of software engineering fundamentals (testing, packaging, code reviews).
• Ability to translate ambiguous business problems into rigorous modeling plans and deliver results.
• High attention to detail and an ownership mindset in managing multiple high-impact projects.

Preferred Qualifications

• Experience with graph neural networks (PyG/DGL), spatiotemporal GNNs, or temporal graph learning.
• Experience with causal inference or decision-focused forecasting (uplift, impact estimation, counterfactuals, policy evaluation).
• Familiarity with large-scale data/compute: Spark, distributed training, feature stores, GPU workflows.
• Experience building human-centered explainability: dashboards, driver decomposition, “why changed” analysis, model cards.
• Publishations, patents, or open-source contributions in time series, XAI, or graph learning.

Key Skills / Tech Stack

• Proficiency in Python, Sql and data visualization tools.
• Experience using PyTorch/TensorFlow; scikit-learn; XGBoost/LightGBM and other models for production grade models.
• Experience building solutions with time series libraries (statsmodels, Prophet-like tools, etc.).
• Interest and exposure to explainability: SHAP, Integrated Gradients, permutation importance, counterfactuals.

Nice to have

• Experience with data platforms like Spark/Databricks, Airflow, Kubernetes.
• MLOps/AgentOps experience in deployment model and/or Agentic workflows at scale.

Minimum Qualifications

• Option 1: Bachelors degree in Statistics, Economics, Analytics, Mathematics, Computer Science, Information Technology or related field and 4 years' experience in an analytics related field.
• Option 2: Masters degree in Statistics, Economics, Analytics, Mathematics, Computer Science, Information Technology or related field and 2 years' experience in an analytics related field.
• Option 3: 6 years' experience in an analytics or related field.

Additional Compensation

The annual salary range for this position is $143,000.00 - $286,000.00 Additional compensation includes annual or quarterly performance bonuses. Additional compensation for certain positions may also include : Stock

Benefits

• Health benefits include medical, vision and dental coverage.
• Financial benefits include 401(k), stock purchase and company-paid life insurance.
• Paid time off benefits include PTO (including sick leave), parental leave, family care leave, bereavement, jury duty, and voting.
• Other benefits include short-term and long-term disability, company discounts, Military Leave Pay, adoption and surrogacy expense reimbursement, and more.

Company

We are committed to maintaining a drug-free workplace and has a no tolerance policy regarding the use of illegal drugs and alcohol on the job. This policy applies to all employees and aims to create a safe and productive work environment.