Lead Data Scientist - Merchandising & Pricing (REMOTE)

Overview

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?

Job Purpose

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.

Responsibilities

• 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.

Preferred Qualifications

• 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
• 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)

Virtual Requirements

To ensure a smooth and secure experience, please note the following:

• Cameras must be on during all virtual interviews.
• AI tools are not permitted to be used by the candidate during any part of the interview process.

Targeted Pay Range

$95,200.00 - $158,800.00. This is part of a competitive total rewards package that could include other components such as: incentive, equity and benefits. Individual pay is determined by a number of factors including experience, location, internal pay equity, and other relevant business considerations. We review all teammate pay regularly to ensure competitive and equitable pay.

We Offer

• DICK'S Sporting Goods complies with all state paid leave requirements.
• We also offer a generous suite of benefits.
• To learn more, visit www.benefityourliferesources.com.