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

Target • Sunnyvale, CA

3d • Hybrid

About The Position

The Sr Applied Data Scientist on Target’s Search and Browse Applied Data Science team develops and manages predictive algorithms that power Target’s digital search, browse, relevance, retrieval, ranking, and personalization experiences across mobile and web. The team builds scalable machine learning systems for product discovery, semantic search, and customer engagement across large, constantly changing retail catalogs, including natural-language and long-tail search, zero-shot item understanding, and AI-native commerce discovery operating at 10K+ queries per second under strict latency and reliability requirements.

The role develops and deploys production machine learning models and systems, builds feature pipelines and evaluation workflows, and uses experimentation and analysis to improve relevance, engagement, conversion, and customer satisfaction. It applies modern approaches such as embeddings, retrieval and ranking models, transformers, NLP, vector search, and GenAI/RAG. The scientist partners with product, engineering, and infrastructure teams to align technical solutions with business and operational priorities, balancing model quality with latency, scalability, reliability, and infrastructure efficiency. The role also contributes to machine learning engineering and experimentation practices, mentors junior scientists, and collaborates across teams.

The position uses Target’s Hybrid/Flex for Your Day arrangement. Work is performed both onsite at Target HQ in Sunnyvale, California, or the assigned Minnesota location, and virtually, as role, team, and daily tasks require. Work duties cannot be performed outside the country of the primary work location unless otherwise prescribed by Target. The listed pay range is $118,000 to $211,000; pay may vary based on factors including labor markets, education, work experience, and certifications.

Requirements

• PhD or MS in Computer Science, Statistics, Applied Mathematics, Physics, or a related quantitative discipline.
• At least 3 years of industry experience in Machine Learning, Data Science, Search, NLP, Personalization, or related machine learning systems.
• Exceptional experience and understanding of retrieval/ranking systems, semantic search, NLP, vector search, or related machine learning domains.
• Strong coding skills in Python and SQL, with experience in distributed data processing ecosystems.
• Demonstrated experience building and deploying end-to-end production machine learning systems.
• Strong understanding of production machine learning system tradeoffs, including latency, scalability, reliability, and operational excellence.
• Experience designing experiments and interpreting online and offline metrics.
• Strong problem-solving skills and ability to independently drive projects in moderately ambiguous environments.
• Very good communication and cross-functional collaboration skills.
• Constant learner mentality, with current knowledge of evolving AI technologies through formal training and self-directed education.

Nice To Haves

• Experience with modern machine learning approaches such as embeddings, transformers, semantic retrieval, RAG systems, or GenAI technologies.

Responsibilities

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

Benefits

• Eligible team members and dependents may receive medical, vision, dental, and life insurance and other health benefits and programs.
• 401(k).
• Employee discount.
• Short-term disability and long-term disability.
• Paid sick leave.
• Paid national holidays.
• Paid vacation.
• Financial, education, and well-being benefits.

What This Job Offers

• Job Type: Full-time
• Career Level: Senior