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

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.

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.

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 and understanding with retrieval/ranking systems, semantic search, NLP, vector search, or related ML domains

Strong demonstrated coding skills in Python and SQL with experience in distributed data processing ecosystems

Demonstrated experience building and deploying end to end production ML systems

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

This position will operate as a Hybrid/Flex for Your Day work arrangement based on Target’s needs. A Hybrid/Flex for Your Day work arrangement means the team member’s core role will need to be performed both onsite at the Target HQ in Sunnyvale, CA or MN location the role is assigned to and virtually, depending upon what your role, team and tasks require for that day. Work duties cannot be performed outside of the country of the primary work location, unless otherwise prescribed by Target. Click here if you are curious to learn more about Minnesota.

Target will consider for employment qualified applicants with criminal histories in a manner consistent with the San Francisco and City of Los Angeles Fair Chance Ordinances.

Benefits Eligibility

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Americans with Disabilities Act (ADA)

In compliance with state and federal laws, Target will make reasonable accommodations for applicants with disabilities. If a reasonable accommodation is needed to participate in the job application or interview process, please reach out to [email protected]. Non-accommodation-related requests, such as application follow-ups or technical issues, will not be addressed through this channel.