Retail Predictive Analytics Data Scientist

Python, Statistics, Machine Learning (ML), R Programming Language, Statistical Analysis, Data Science, Econometrics, Data Analysis, Predictive Analytics, Natural Language Processing · I need a data scientist who can build a robust predictive analytics solution for a retail-focused use case. My datasets include sales transactions, product metadata, marketing campaign logs, customer reviews, and social media chatter, so you will be working with a mix of structured tables and unstructured text.

Your mission is to extract the signals that drive sell-through, forecast demand at SKU and store level, and surface actionable insights for merchandising and marketing. I expect you to handle everything from data ingestion and cleaning through to model deployment, with clear documentation of assumptions and feature engineering steps.

You may use Python (pandas, scikit-learn, XGBoost, Prophet, TensorFlow, or similar), SQL for warehousing, and NLP libraries such as spaCy or transformers for the text components. If you prefer R or another stack, I’m open as long as the final model meets the accuracy and interpretability goals.

Deliverables • Cleaned and well-structured datasets ready for modelling • Reproducible notebooks / scripts showing EDA, feature engineering, and model training • A predictive model (or ensemble) with validated performance metrics • Summary repo