Senior Data Scientist

Role Overview

• Design and deploy Statistical and Machine Learning 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 problem scoping, data preparation and feature engineering, model development and experimentation, monitoring, back-testing and validations, along with performance optimizations.
• Develop advanced time-series solutions using: Statistical models: ETS, ARIMA/SARIMA, State Space Models; ML approaches: Gradient Boosting (XGBoost/LightGBM), Random Forests, linear/elastic models with time-based features; Deep learning models: LSTM/GRU, Temporal Convolutional Networks (TCNs), transformer-based models such as TimesFM.
• Implement probabilistic forecasting approaches and uncertainty quantification including quantile regression, Bayesian techniques, conformal prediction, and 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 ML Ops 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 junior data scientists, set modeling standards, and influence technical direction across teams.

Requirements

• 6+ years of experience in Data Science / Applied ML, with deep hands-on exposure to forecasting and predictive modeling.
• Demonstrated experience delivering production grade ML models with measurable business outcomes.
• Strong functional knowledge of time series topics: seasonality, hierarchies, intermittent demand, holidays/events, promotions, missingness, outliers, anomaly detection, and regime changes.
• Ability to translate ambiguous business problems into rigorous modeling plans and deliver results.
• 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).
• Experience with CI/CD pipelines, containerization, and orchestration, including tools such as Git and Kubernetes.
• Experience developing and operating distributed machine learning systems at scale, including training, inference, and serving.
• Ability to collaborate with stakeholders across the organization to advocate for and implement trustworthy AI/ML practices.
• Strong communication skills, with experience presenting technical concepts, research findings, and product insights to technical and non-technical audiences.
• A research-driven, detail-oriented mindset, balanced with a bias toward execution and real-world impact.
• Commitment to engineering excellence, including automation, standards, and continuous improvement.
• A collaborative and ownership-oriented approach, with a history of clear communication and effective teamwork.
• High attention to detail and an ownership mindset in managing multiple high-impact projects.
• A passion for lifelong learning, staying current with advances in AI/ML and GenAI, and engaging with the broader research and open-source communities.
• 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.

Tech Stack

• Kubernetes
• Python
• PyTorch
• Scikit-Learn
• SQL
• Tensorflow

Benefits

• Other great perks include a host of best-in-class benefits maternity and parental leave
• PTO
• health benefits