Are you a passionate technologist with experience in AI, Machine Learning, Data Science and Analysis? Are you looking for an opportunity to drive enterprise impact and shape the future of a leading sports retailer with $12B+ in revenue and 800+ physical stores? Do you enjoy working with a highly skilled team of Machine Learning engineers & Scientists, co-creating enterprise grade AI capabilities?

As the Lead Data Scientist - Merchandising & Pricing, you will be a key technical leader in our teammate transformation that aims to deliver a best-in-class teammate experience by providing them advanced intelligent decisioning tools using AI/GenAI and Machine Learning at its core. This is an exceptional opportunity not only to transform the way we deliver omnichannel Merchandising and Pricing by building foundational AI/GenAI capabilities, but also to do career defining work in the space.  

This role will require an emerging technical leader & SME with strong experience in traditional Machine Learning algorithms along with deep understanding of the cutting edge SOTA AI/GenAI methods used in Retail merchandising and Pricing data science initiatives. As a technical leader you will be influencing critical enterprise technical strategies both in the Machine Learning/AI space and neighboring spaces like forecasting, optimization, NLP, webservices, integrations with applications and data systems etc. You will partner with product, business, and engineering leads to design and implement data science powered intelligent tools for merchandising and pricing business partners and scale and help them understand the art of the possible with AI technology through deep technical design.

• Advanced Data Science Leadership: Lead design and implementation of advanced data science algorithms that improve merchandising and pricing business decisions, including building models for Demand forecasting, Assortment optimization, Price elasticity, and Inventory allocation and replenishment. 

• Developing & Optimizing Demand Forecasting models: Designing and deploying demand forecasting algorithms that go beyond univariate time series to multivariate and hierarchical forecasts for predicting long range, multi-echelon sales forecasting, and that can handle cold start problems, reconciliation at all levels and works at scale.   

• Assortment Planning & Optimization: Develop & Implement AI/ML driven assortment selection algorithms that learn from user behavior & preferences to deliver tailored assortment choices based on user metadata like location, past site behavior etc. and that are optimized for the capacity, variety, sales targets and other business constraints. 

• Natural Language Process (NLP) & GenAI: Collaborate with product & data engineers to identify data for modeling, and transform datasets as required for effective modeling, like creating identifying and enriching product attributes using NLP and LLMs. Creating feature stores and vector embedding used for Product associations and segmentation, and other modeling needs. 

• Machine Learning & Deep Learning: Build, scale and deploy robust Machine Learning models leveraging Classification, Regression, and Clustering, Context understanding, techniques to drive data-driven decision-making across diverse retail business functions. Leverage deep learning models for building complex forecasting and other predictive use cases.   

• Price Elasticity & Casual Inference: Develop models to process historical and large datasets to understand model Price elastic demand for products, categories, channels and customer segments using predictive and causal modeling techniques. Deliver actionable elasticity estimates and counterfactual analyses to inform pricing optimization, promotional strategies, and markdown decisions to monitor the performance of forecasting and other predictive models in real time, detect anomalies, ensuring data drift, concept drift, and addressing technical issues to maintain the efficiency & effectiveness of model predictions.  

• Experimentation & A/B Testing: Collaborate with analytics, product and business teams to champion a test-and-learn approach by designing and executing structured experiments to validate model hypotheses, measure business impact, and drive continuous improvement. 

• Research & Development of Emerging Technologies: Staying updated with the latest advancements in AI, ML technologies and exploring opportunities to incorporate these innovations into Merchandising and Pricing transformation initiatives. 

• Master's Degree or Equivalent Level in quantitative fields like computer science, engineering, physics, mathematics, etc. 

• 6+ years of experience in the field with at least 2-3 years of being the main technical lead in related projects 

• Experience working with SOTA machine learning, deep learning (LSTM, Transformers), Optimization models for retail and ecommerce use cases driving efficiency in operations and customer value. 

• Experience with Large Language models and Generative AI and Agents. Bonus if specific experience in operations research. 

• Experience in ML Ops model monitoring, retraining, CI/CD, and experiment tracking  

• Extensive experience using common machine learning and deep learning frameworks such as TensorFlow, PyTorch, OpenAI, and LangChain 

• Expert understanding of Python and other common languages.  

• Expert level experience in cloud platforms like Databricks, GCP, and offers like Azure ML, Vertex AI.  

• Experience being the technical lead of multiple projects at the same time, responsible for delivery and business metrics 

• Experience in an Agile working environment and at least one related project management tool (Azure, DevOps, Jira, etc.) 

• Previous experience mentoring, training, and developing junior members of the team through technical influence. 

• Experience with software engineering principles as it relates to Machine Learning systems. 

• Comfortable presenting results to and influencing senior and executive leadership on strategic technical decisions, from the lens of science. 

• Brings a collaborative, problem solving and growth mindset to all interactions with a strong focus on delivery. 

QUALIFICATIONS:

• Education: Master's Degree or equivalent level preferred

• General Experience: Substantial general work experience together with comprehensive job related experience in own area of expertise to fully competent level. (Over 6 years to 10 years)

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