AI Data Scientist for Retail, Merchandising, & Pricing
Budget: $1,500 – $3,000 USD
Looking for a highly skilled retail, merchandising, and pricing Data Scientist with deep expertise in AI, Generative AI, and NLP, who has hands-on experience building the following and can walk through in-depth examples and solutions they have implemented across diverse real-world scenarios to drive scalable retail, merchandising, and pricing initiatives, with strong expertise in forecasting, optimization, assortment, and inventory management.
- Walk us through an in-depth end-to-end implementation of each of the points mentioned below.
Advanced Forecasting Models & Time Series: Design and deploy univariate, multivariate, and hierarchical demand forecasting algorithms for long-range, multi-echelon predictions. Handle cold-start problems, cross-level reconciliation, and scale across large datasets.
Optimization Models: Build models for price elasticity, inventory allocation, replenishment optimization, and operational efficiency improvements. Apply operations research techniques where applicable.
Assortment Planning & Optimization: Develop AI/ML-driven assortment selection algorithms that learn from user behavior and preferences, delivering tailored assortment choices optimized for capacity, variety, sales targets, and other business constraints.
Machine Learning & Deep Learning: Build, scale, and deploy ML models leveraging classification, regression, clustering, and context understanding. Leverage deep learning techniques, including LSTM and Transformer-based models, for complex forecasting and predictive use cases.
Causal Inference & Price Elasticity: Develop models to process historical and large datasets to understand price-sensitive demand for products, categories, channels, and customer segments. Deliver actionable elasticity estimates and counterfactual analyses to inform pricing optimization, promotions, and markdown strategies.
Experimentation & A/B Testing: Collaborate with analytics, product, and business teams to design and execute structured experiments, validate model hypotheses, measure business impact, and drive continuous improvement.
Natural Language Processing (NLP) & Generative AI: Collaborate with product and data engineers to identify and transform datasets, enrich product attributes, create feature stores and vector embeddings, and apply LLMs for product associations, segmentation, and other modeling needs.
Full ML Lifecycle Management: Monitor, retrain, and maintain models, including experiment tracking, CI/CD pipelines, anomaly detection, and managing data/concept drift to ensure efficiency and accuracy.
Deployment & Integration: Integrate solutions with applications and data systems via APIs and web services, ensuring scalability and reliability.
Research & Development of Emerging Technologies: Stay updated on the latest AI/ML advancements and explore opportunities to incorporate innovations into merchandising and pricing transformation initiatives.
Frameworks & Tools: TensorFlow, PyTorch, OpenAI, LangChain, and other modern ML infrastructure.
- Walk us through an in-depth end-to-end implementation of each of the points mentioned below.
Advanced Forecasting Models & Time Series: Design and deploy univariate, multivariate, and hierarchical demand forecasting algorithms for long-range, multi-echelon predictions. Handle cold-start problems, cross-level reconciliation, and scale across large datasets.
Optimization Models: Build models for price elasticity, inventory allocation, replenishment optimization, and operational efficiency improvements. Apply operations research techniques where applicable.
Assortment Planning & Optimization: Develop AI/ML-driven assortment selection algorithms that learn from user behavior and preferences, delivering tailored assortment choices optimized for capacity, variety, sales targets, and other business constraints.
Machine Learning & Deep Learning: Build, scale, and deploy ML models leveraging classification, regression, clustering, and context understanding. Leverage deep learning techniques, including LSTM and Transformer-based models, for complex forecasting and predictive use cases.
Causal Inference & Price Elasticity: Develop models to process historical and large datasets to understand price-sensitive demand for products, categories, channels, and customer segments. Deliver actionable elasticity estimates and counterfactual analyses to inform pricing optimization, promotions, and markdown strategies.
Experimentation & A/B Testing: Collaborate with analytics, product, and business teams to design and execute structured experiments, validate model hypotheses, measure business impact, and drive continuous improvement.
Natural Language Processing (NLP) & Generative AI: Collaborate with product and data engineers to identify and transform datasets, enrich product attributes, create feature stores and vector embeddings, and apply LLMs for product associations, segmentation, and other modeling needs.
Full ML Lifecycle Management: Monitor, retrain, and maintain models, including experiment tracking, CI/CD pipelines, anomaly detection, and managing data/concept drift to ensure efficiency and accuracy.
Deployment & Integration: Integrate solutions with applications and data systems via APIs and web services, ensuring scalability and reliability.
Research & Development of Emerging Technologies: Stay updated on the latest AI/ML advancements and explore opportunities to incorporate innovations into merchandising and pricing transformation initiatives.
Frameworks & Tools: TensorFlow, PyTorch, OpenAI, LangChain, and other modern ML infrastructure.