Key Responsibilities
- Develop AI-powered systems using statistical modeling and regression techniques to process operational data and drive fulfillment network decisions
- Design and validate time series forecasting models, evaluating accuracy, bias, and stability for real-world deployment
- Integrate large language models (LLMs) via APIs into backend workflows, focusing on structured prompting and output parsing
- Build and maintain Python data pipelines to normalize transactional data from e-commerce platforms (Shopify, Amazon SP-API) into analysis-ready datasets
- Create lightweight internal interfaces using React or Streamlit to surface model outputs to non-technical teams
- Implement robust validation frameworks to ensure model reliability across varying data patterns (stable, seasonal, intermittent, spiky)
Requirements
- Proficiency in Python with experience building scalable data pipelines and statistical models
- Strong foundation in statistical concepts: hypothesis testing, distributional assumptions, confidence intervals, and error decomposition
- Hands-on experience integrating LLMs into operational systems via API calls
- Familiarity with e-commerce platform APIs and data normalization techniques
- Ability to explain model predictions and pipeline architecture for non-technical stakeholders