Lead Data Scientist, Search (REMOTE)

At DICK’S Sporting Goods, we believe in how positively sports can change lives. On our team, everyone plays a critical role in creating confidence and excitement by personally equipping all athletes to achieve their dreams. We are committed to creating an inclusive and diverse workforce, reflecting the communities we serve.

If you are ready to make a difference as part of the world’s greatest sports team, apply to join our team today!

OVERVIEW:

Founded in 1948, DICK’S Sporting Goods first started as a bait-and-tackle shop in Binghamton, NY and has since rapidly expanded into a leading omnichannel retailer with more than 850 locations representing our multiple brands: DICK’S, House of Sport, Golf Galaxy, Public Lands, Going Going Gone, and more. Over the years, it’s been our relentless focus on inspiring, supporting and equipping athletes and outdoor enthusiasts to achieve their dreams that has allowed us to become the $13B company we are today.

Our company is looking to invest in our future as we embark on a journey from being the best sports retailer in the world to becoming the best sports company in the world. We aim to build the ultimate athlete data set that will power our tools and platforms for the most personalized athlete experiences. Join us as we transform our technology, data and analytics to build next-gen tools and platforms for our athletes and teammates.

About the Position:

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, Search, you will be a key technical leader in our athlete and teammate transformation that aims to deliver a best in class customer and teammate search experience by providing them advanced intelligent decisioning tools using AI/GenAI and Machine Learning at its core. This is an exceptional opportunity to transform the way we deliver omnichannel search by building foundational AI/GenAI capabilities and do career defining work in the space.

Job Purpose:

This role will require an emerging technical leader & subject matter expert with strong experience in traditional Machine Learning algorithms along with deep understanding of the cutting edge SOTA AI/GenAI methods used in search engines. As a technical leader you will be influencing critical enterprise technical strategies both in the Machine Learning/AI space and neighboring integration spaces like frontend, search engineering, DSP, backend data systems etc. You will partner with product, business, and engineering leads to design search systems for future growth and scale and help them understand the art of the possible on search experiences with AI technology through deep technical design.

Responsibilities:

• Lead design and implementation of advanced algorithms that improve multi-modal search performance, including using embeddings, graph learning, deep learning and large language models (LLMs), by refining query understanding, retrieval, ranking and whole page optimization.

• Developing & Optimizing Ranking Algorithms: Designing and deploying ranking algorithms that go beyond keyword matching to consider content quality, user experience and various site-level factors for determining the order of search results.

• Search Personalization: Develop & Implement AI/ML driven search personalization algorithms that learn from user behavior & preferences to deliver tailored search results based on user metadata like location, past site behavior etc.

• Expertise in data systems: Collaborate with data & search engineers to build robust data pipelines for collecting, transforming & managing massive datasets required for efficient operation of search systems. This includes unique challenges of integrating & synchronizing multimodal data streams.

• Search Architecture Design & Optimization: Collaborating with software engineers and architects to design and optimize the underlying infrastructure supporting the search engine.

• Performance Monitoring & Optimization: Developing and implementing tools & processes to monitor the performance of search systems in real time, detecting anomalies, ensuring data quality, and addressing technical issues to maintain the efficiency & effectivenss of the search system.

• Experimentation & A/B testing: Collaborate with analytics teams to design, conduct and analyze controlled experiments that evaluate search performance, bringing in deeper insights that help validate hypotheses about impact of search algorithms.

• Research & development of emerging technologies: Staying updated with the latest advancements in AI, ML & search technologies and exploring opportunities to incorporate these innovations into search systems.

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