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

Target•Brooklyn Park, MN

11d•$168,000 - $356,000•Hybrid

About The Position

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.

Requirements

PhD or MS in Computer Science, Statistics, Applied Mathematics, Physics or related quantitative discipline
8+ years of industry experience building and scaling ML systems for Search, Recommendation, Personalization, Ads, or AI platforms
Strong experience with semantic retrieval, vector search, RAG systems, conversational search or agentic AI systems
Deep expertise in retrieval/ranking architectures, recommendation systems, semantic search, NLP/LLMs, experimentation and ML infrastructure (VertexAI)
Demonstrated Python programming and technical ML problem-solving skills
Proven experience defining architecture and long-term strategy for large-scale production AI systems
Experience operating large-scale online systems with strict latency, scalability, reliability, and infrastructure cost requirements
Strong understanding of retrieval/ranking system tradeoffs, experimentation strategy, and operational excellence
Strong track record driving measurable business impact through ML innovation
Experience leading cross-functional initiatives spanning multiple teams or organizations
Ability to influence executive stakeholders and drive organizational technical direction
Exceptional communication, technical leadership, strategic thinking, and mentoring skills

Responsibilities

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