Level

Target

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

Target is hiring a Sr Applied Data Scientist - Search and Browse (applied ML, LLMs, ML Ops). It pays $118k-$211k a year and Level rates it ; you can apply on Target.

Level's read on this job

  • Builds AI ●●●● is how Level rates this data scientist role: the job is building AI systems.
  • The posting mentions RAG, MLOps and NLP among its tools and skills.
  • Level classes it as a mid-level role.
  • Target has 1,600+ other open roles on Level.
  • Posted in the last few days.

AI in this role

ragml-opsnlp
The pay range is $118,000.00 - $211,000.00

Pay is based on several factors which vary based on position. These include labor markets and in some instances may include education, work experience and certifications. In addition to your pay, Target cares about and invests in you as a team member, so that you can take care of yourself and your family. Target offers eligible team members and their dependents comprehensive health benefits and programs, which may include medical, vision, dental, life insurance and more, to help you and your family take care of your whole selves. Other benefits for eligible team members include 401(k), employee discount, short term disability, long term disability, paid sick leave, paid national holidays, and paid vacation. Find competitive benefits from financial and education to well-being and beyond at https://corporate.target.com/careers/benefits.

JOIN TARGET AS A SR APPLIED DATA SCIENTIST – SEARCH RANKING

About Us:

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. 


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.


As a Sr Data Scientist – Search you will:

  • 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.


About you:

  • 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

Please paste this url into your preferred browser to learn about benefits eligibility for this role: https://tgt.biz/BenefitsForYou_D

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 candidate.accommodations@HRHelp.Target.com. Non-accommodation-related requests, such as application follow-ups or technical issues, will not be addressed through this channel.  

How we rate this

Sr Applied Data Scientist - Search and Browse (applied ML, LLMs, ML Ops) at Target rates 99 out of 100 for how much of the daily work is AI. That makes it Builds AI (AI Level 4 of 4). The level is about AI in the job, not seniority.

Classification

Builds AI. The job is building AI systems.

  1. ●●●● Builds AI80 to 100
  2. ●●●○ Works on AI60 to 79
  3. ●●○○ Uses AI40 to 59
  4. ●○○○ Little AI0 to 39

Levels come from how often the tools, models and workflows of the role are named in the posting itself. Read the description and count the AI tools, models and workflows it names.

Prepare for this job

Built from this posting and from similar jobs on Level.

Skills and AI tools this role asks for

RAGML OpsNLP

Also common in data scientist jobs at this level

Pay

This job starts at
$118k
Typical data scientist job
$170k

This posting starts 31% below the typical data scientist job. See data scientist jobs

Questions you could be asked

  1. How would you design a retrieval step so the model answers from real data instead of guessing?
  2. How do you monitor a model once it's live, and how do you know it needs retraining?
  3. What NLP problem have you worked on, and how did you measure whether it actually worked?
  4. How would you decide a model or AI system is ready to ship?
  5. Tell me about a time a model underperformed in production. How did you find out, and what did you change?

Adapt your resume

  • List these exact terms on your resume: RAG, ML Ops, and NLP. An applicant tracking system matches the wording, not the idea.
  • Attach one line of real, concrete experience to at least one of them. A tool named with nothing behind it rarely survives a human read.
  • Lead with what you built, trained or shipped. This role is judged on the AI system itself, not the tools around it.

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