Manager, Data Scientist

As a Senior Data Scientist supporting Walmart’s Global eCommerce business, you will be at the forefront of designing and implementing data-driven solutions that directly impact the category management, customer experience, pricing, supply chain, search, marketing, and merchandising operations across Walmart’s digital platforms. You will apply advanced machine learning, statistical modeling, and optimization techniques to drive business strategy and deliver measurable value at scale.

Key Responsibilities:

1. Advanced Analytics & Modeling

• Model Development & Deployment: Design, develop, and deploy robust machine learning and deep learning models to address a wide array of eCommerce challenges. This includes, but is not limited to, seller performance optimization, personalization engines, demand forecasting, customer segmentation, pricing optimization, and fraud detection.
• Develop algorithms that improve personalization, seller performance optimization, dynamic commission and incentive optimization, seller churn prediction and retention, product listing scoring, search ranking, recommendation systems, and customer segmentation.
• Build real-time and batch solutions with large-scale datasets from multi-modal sources (clickstream, transaction, customer, inventory, etc.).

2. Business Problem Solving

• Translate complex business problems into analytical frameworks and hypotheses.
• Partner with business stakeholders (category, sales, marketing, product, logistic) to identify opportunities for applying data science for customer and business value.
• Quantify business impact and present findings and models to senior leadership with actionable insights.

3. Data Engineering & Infrastructure Collaboration

• Work closely with data engineering and MLOps teams to ensure models are production-ready and maintainable.
• Ensure data quality, feature availability, and model retraining pipelines are robust and automated.

4. Experimentation & Causal Inference

• Design and analyze A/B tests, quasi-experiments, and time-series interventions to measure impact of business changes.
• Apply techniques such as propensity scoring, synthetic controls, Bayesian inference, and uplift modeling to estimate causal effects.

5. Technical Mentorship

• Mentor junior data scientists and contribute to team knowledge-sharing and best practices.
• Peer-review code, models, and documentation; lead technical workshops.

Required Qualifications:

Education:

· Master or above in Computer Science, Statistics, Applied Mathematics, Operations Research, or related quantitative fields.

Experience:

• 5+ years of professional experience in applied data science or machine learning roles.
• Proven track record of delivering business-impacting models in eCommerce, retail, digital marketplaces, or related industries.
• Experience with production-grade model deployment and monitoring at scale.

Technical Skills:

• Programming: Expert in Python (Pandas, NumPy, scikit-learn, XGBoost, etc.), SQL; familiar with Java/Scala/R is a plus.
• ML/AI Frameworks: Proficient in TensorFlow, PyTorch, or similar deep learning frameworks.
• Statistical Modeling: Time series forecasting, GLMs, survival analysis, regression, clustering, etc.
• Experimentation: A/B testing, uplift modeling, causal inference.
• Big Data Ecosystem: Spark, Hive, Presto, Hadoop, or equivalent distributed computing tools.
• Cloud & MLOps: Experience with cloud platforms (GCP, Azure, or AWS), Airflow, MLflow, Docker/Kubernetes for model lifecycle management.

Preferred Qualifications:

• Experience in eCommerce use cases: dynamic pricing, product ranking, inventory forecasting, recommender systems, customer lifetime value modeling.
• Familiarity with product analytics and user journey data (e.g., Google Analytics, Adobe Analytics).
• Experience with real-time decisioning systems.
• Strong presentation and storytelling skills with ability to explain technical concepts to non-technical stakeholders.