Sr Applied Data Scientist - Search and Browse (Applied ML, NLP, LLMs)

• Develop and deploy scalable ML models for search ranking, browse personalization, semantic retrieval, and query understanding systems
• Design and execute offline and online experiments to improve relevance, engagement, conversion, and customer satisfaction
• Build scalable feature pipelines, evaluation frameworks, and ML workflows for production systems
• Apply modern ML techniques including embeddings, retrieval/ranking models, transformers, NLP, vector search, and GenAI/RAG systems
• Improve query understanding, catalog understanding, semantic retrieval, and long-tail search relevance across large retail catalogs
• Partner closely with Product, Engineering, and Infrastructure teams to align technical solutions with business priorities and operational requirements
• Drive data-informed decision making through deep analysis, experimentation, and business impact measurement
• Balance model quality with latency, scalability, reliability, and infrastructure efficiency in large-scale production systems
• Contribute to best practices in experimentation, ML engineering, and operational excellence
• Mentor junior scientists and collaborate across teams to improve Search and Browse experiences

Core responsibilities of this job are articulated within this job description. Job duties may change at any time due to business needs.

• PhD or MS in Computer Science, Statistics, Applied Mathematics, Physics or related quantitative discipline
• 3+ years of industry experience in Machine Learning, Data Science, Search, NLP, Personalization or related ML systems
• Exceptional experience with retrieval/ranking systems, semantic search, NLP, vector search, or related ML domains
• Strong coding skills in Python and SQL with experience in distributed data processing ecosystems
• Demonstrated hands-on experience building and deploying production ML systems at scale
• Strong understanding of production ML system tradeoffs including latency, scalability, reliability, and operational excellence
• Experience with modern ML approaches such as embeddings, transformers, semantic retrieval, RAG systems, or GenAI technologies
• Experience designing experiments and interpreting online/offline metrics
• Strong problem-solving skills with the ability to independently drive projects in moderately ambiguous environments
• Very good communication and cross-functional collaboration skills
• Constant learner mentality who stays current with new and evolving AI technologies via formal training and self-directed education

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 (https://corporate.target.com/about) .

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 core relevance, retrieval, ranking, and personalization systems that power Target’s Digital experience. We develop scalable ML systems that improve product discovery, semantic search, browse relevance, and customer engagement across Mobile and Web platforms.

E-commerce Search is rapidly evolving with advances in LLMs, semantic retrieval, and conversational AI. We are building next-generation discovery experiences that help millions of guests find the right products quickly and intuitively across massive and constantly changing retail catalogs.

Our team is solving large-scale Search challenges at enterprise scale, including natural language and long-tail search, semantic retrieval, zero-shot item understanding, and AI-native commerce discovery experiences operating at 10K+ QPS with strict latency and reliability requirements.