Principal Applied Data Scientist - Search and Browse (NLP, Vector Search, LLMs)

The pay range is $168,000.00 - $356,000.00 Pay is based on several factors which vary based on position. These include labor markets and in some instances may include

, work experience and certifications. In addition to your pay, Target cares about and invests in you as a team member, so that you can take care of yourself and your family. Target offers eligible team members and their dependents comprehensive health

and programs, which may include medical, vision, dental, life insurance and more, to help you and your family take care of your whole selves. Other

for eligible team members include 401(k), employee discount, short term disability, long term disability, paid sick leave, paid national holidays, and paid vacation. Find competitive

from financial and

to well-being and beyond at https://corporate.target.com/careers/

. JOIN TARGET AS A PRINCIPAL DATA SCIENTIST – SEARCH AND BROWSE

Working at Target means helping all families discover the joy of everyday life. We bring that vision to life through our values and culture. Learn more about Target here. A role with Applied Data Sciences team at Target means the chance to help develop and manage state of the art predictive algorithms that use data at scale to automate and optimize decisions at scale. Whether you join our Search, RecSys, Supply Chain Optimization or Machine Learning teams, you’ll be challenged to harness Target’s impressive data breadth to build the algorithms that power solutions our partners in Marketing, Supply Chain Optimization, Network Security and Personalization rely on. Every Scientist on Target’s Data Sciences team can expect modeling and data science, software/product development of highly performant code for Model Performance, and to elevate Target’s culture and apply retail domain knowledge. About Our Search And Browse Team The Search and Browse Applied Data Science team builds the foundational relevance, retrieval, ranking, and personalized search systems that power Target’s Digital experience at scale. We are defining the future of AI-native commerce discovery across Search, Browse and emerging conversational shopping experiences. E-commerce Search Is Undergoing a Massive Transformation, And We Are Building The Architecture To Lead It. We Are Solving Some Of The Hardest Problems In Retail AI At a $10B+ Commercial Scale And 10K+ QPS, Including • Architecting search and data systems for external LLM ingestion so our catalog wins in ChatGPT, Gemini, agentic commerce, and the next generation of AI-native discovery experiences • Building zero-shot and cold-start discovery systems for rapidly changing, seasonal retail inventory • Solving natural language and long-tail search problems where conversational queries and traditional retrieval systems break against massive unstructured product catalogs • Evolving multi-stage retrieval and ranking architectures that balance relevance quality, latency, scalability, and infrastructure efficiency at enterprise scale We are looking for pragmatic builders and technical leaders who thrive on shipping production systems at scale. Engineering excellence, sub-second latency, operational reliability, infrastructure economics, and seamless integration with core Retrieval and Ranking systems matter just as much as modeling sophistication. This role requires deep technical expertise, exceptional product judgment, and the ability to influence organizational strategy while driving measurable customer and business impact As a Principal Data Scientist – Search and Browse you’ll: • Define the long-term technical vision and organizational roadmap for Search, Browse, and AI-driven discovery systems • Lead architecture strategy for large-scale retrieval, ranking, semantic search, and GenAI systems operating at massive scale • Drive innovation across transformers, LLMs, RAG architectures, multi-stage ranking systems, personalization, conversational commerce, and agentic AI • Architect search and retrieval systems for external LLM and agentic search ecosystem integration • Define the future evolution of semantic retrieval, conversational search, and zero-shot discovery systems • Influence organization-wide strategy across relevance, experimentation, evaluation, ML infrastructure, and AI product investments • Lead highly ambiguous, multi-quarter initiatives involving Product, Engineering, Applied Science, Infrastructure, and executive stakeholders • Establish scalable ML architecture patterns, experimentation standards, and operational best practices across multiple teams • Drive foundational investments and technical direction across Search, Retrieval Infrastructure, and AI-powered discovery platforms • Balance customer experience, business impact, system reliability, latency, scalability, and infrastructure cost at enterprise scale • Mentor Lead scientists and technical leaders across the organization • Represent the organization in executive reviews and drive alignment on major technical and product decisions • Influence technical direction and investment priorities across multiple teams and organizations Core