Ind Senior, Data Scientist

Position: (IND) SENIOR, DATA SCIENTIST
Location: Bengaluru

Position Summary...

What you'll do...

Enterprise Business Services is invested in building a compact, robust organization that includes service operations and technology solutions for Finance, People, Associate Digital Experience.

We're looking for a  Senior 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 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/Light

GBM), 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.

What will you bring:

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 Tensor Flow) 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/Tensor Flow; scikit-learn; XGBoost/Light

GBM and other models for production grade models.
Experience building solutions with time series libraries (stats models, Prophet-like tools, etc.)
Interest and exposure to explainability: SHAP, Integrated Gradients, permutation importance,…