T

Target

Sells everyday goods and affordable designer styles

Senior Applied Data Scientist: Search Ranking - Multiple Teams

Applied Machine LearningConfirmed live in the last 24 hours
Full-time$118k - $211k

About the Job

Requirements

  • PhD or Master of Science 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, Natural Language Processing, Personalization, or related ML systems.
  • Exceptional experience and understanding of retrieval and ranking systems, semantic search, Natural Language Processing, vector search, or related ML domains.
  • Strong 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, Retrieval-Augmented Generation systems, or Generative AI technologies.
  • Experience designing experiments and interpreting online and offline metrics.
  • Ability to independently drive projects in moderately ambiguous environments.
  • Very good communication and cross-functional collaboration skills.
  • Keep current with evolving AI technologies through formal training and self-directed education.

Responsibilities

  • 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 and ranking models, transformers, Natural Language Processing, vector search, and Generative AI and Retrieval-Augmented Generation 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 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.

About the Company

Target

Sells everyday goods and affordable designer styles

Target sells groceries, clothing, home goods and other everyday products to U.S. shoppers through stores and its website, with an emphasis on affordable style and design. Its own brands include Cat & Jack children's clothing and Good & Gather food, alongside national brands and exclusive designer collections. Customers shop in person or order online for shipping, pickup or delivery, while Target Plus adds products from outside sellers. Merchandise sales drive its revenue, supplemented by Roundel advertising, marketplace fees and paid memberships. The company traces its roots to 1902, opened its first Target store in 1962, and is headquartered in Minneapolis, Minnesota, United States.

Company size
10,001+
Company stage
Not listed
Total funding
Not listed
Headquarters
Minneapolis, MN
Founded
1902

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