Data Scientist

• Analyze and segment intermittent retail demand using sales frequency, zero-sales patterns, and volumes
• Define dedicated forecasting processes from data preparation through forecast output
• Build probabilistic forecasting models predicting sales timing and size
• Benchmark forecasts against Oracle system forecasts using appropriate accuracy metrics
• Characterize products by velocity, seasonality, volatility, and lifecycle
• Build Databricks tuning pipelines and parameter matrices for Oracle Retail AIF settings
• Validate forecasting parameters in Oracle test environments and compare accuracy
• Design and implement decision-tree diagnostic engines for forecast root-cause analysis
• Detect and report seasonality anomalies, inflated demand, eligibility errors, and outlier-handling gaps
• Build and test production-grade diagnostic code and documentation
• Assess product attributes and image similarity to identify look-alike historical items
• Develop new-item forecasting and matching pipelines and assess scalability
• Present results and recommendations

Explore Data Science Consultant Intern – PFE 2027 at RetSci. Review requirements and application details.