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About This Role
The pay range is $132,000\.00 \- $238,000\.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 LEAD DATA SCIENTIST – ADVANCE AI / AI FACTORY
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.
About the role:
Target’s Advanced AI team builds end\-to\-end AI/ML systems that create meaningful business value across the enterprise. These systems may be powered by LLMs, classical machine learning, or deep learning models, and are designed as scalable, reliable, production\-grade applications, including agentic architectures where they add clear value.
As a Lead Data Scientist for Advanced AI, you will help identify, design, develop, evaluate, and scale AI/ML capabilities that drive automation, insight, and action across core business workflows. You will work closely with AI Engineers, Full\-Stack Engineers, product partners, platform teams, security teams, and business stakeholders to translate ambiguous business problems into practical AI/ML solutions.
In this role, you will provide hands\-on data science leadership across Advanced AI initiatives. You will frame problems, define success metrics, explore data, develop modeling approaches, design experiments, evaluate model and system performance, and help guide solutions from prototype to production. You will work across LLM\-powered applications, classical machine learning, deep learning, retrieval\-augmented generation, agentic systems, intelligent automation, and other applied AI patterns where appropriate.
You will also partner with engineering teams to ensure AI/ML solutions are reliable, measurable, maintainable, and aligned to Target’s enterprise standards. This includes contributing to evaluation strategies, model monitoring approaches, feedback loops, human\-in\-the\-loop workflows, and responsible AI practices. You will help shape technical approaches, identify risks, resolve ambiguity, mentor other Data Scientists, and support the evolution of reusable AI/ML patterns for the broader Advanced AI team.
A successful Lead Data Scientist will help deliver production\-grade AI/ML applications that create measurable business value while raising the quality of data science, experimentation, evaluation and applied AI practices across the team.
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 MS in Computer Science, Machine Learning, Artificial Intelligence, Applied Mathematics, Physics or a related technical field preferred
- 7\+ years of hands\-on experience in data science, machine learning, applied AI, or AI/ML systems
- Demonstrated experience developing and evaluating AI/ML solutions including solutions powered by LLMs, classical machine learning models and deep learning models
- Strong proficiency with Python programming in common data science, machine learning and deep learning libraries (Pandas, NumPy, scikit\-learn, PyTorch, TensorFlow, etc.)
- Experience working with LLMs, prompt engineering, retrieval\-augmented generation, agentic workflows, model APIs, embeddings, evaluation frameworks, or AI observability tools is strongly preferred
- Strong understanding of the model development lifecycle, including problem framing, data exploration, feature engineering, model selection, experimentation, validation, evaluation, deployment partnership, monitoring, and continuous improvement
- Ability to define clear success metrics and evaluation strategies for AI/ML systems, including both offline and production\-facing measures of quality, reliability, adoption, and business impact
- Experience deploying models, prototypes, or AI/ML capabilities into production applications
- Ability to translate ambiguous business problems into structured data science and modeling approaches
- Strong communication skills, with the ability to explain complex AI/ML concepts, tradeoffs, risks, and recommendations to technical partners, business stakeholders, and leaders
- Ability to mentor applied data scientist, contribute to technical direction and raise the quality of data science practices within a team
- Experience working in large enterprise environments with data governance, privacy, security, platform, and operational requirements
- Self\-driven and results\-oriented, with strong ownership, sound judgment, curiosity, and the ability to move quickly while maintaining high technical standards
- Collaborative team player with a commitment to continuous learning, knowledge sharing, responsible AI practices, and building reliable AI/ML systems that create business value
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 MN location the role is assigned to and virtually, depending on what the 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.
Benefits Eligibility
Please paste this url into your preferred browser to learn about benefits eligibility for this role: https://tgt.biz/BenefitsForYou\_EAmericans 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.
Application deadline is : 08/27/2026
Salary Context
This $132K-$238K range is above the median for Data Scientist roles in our dataset (median: $160K across 258 roles with salary data).
View full Data Scientist salary data →Role Details
About This Role
Data Scientists extract insights and build predictive models from data. In the AI era, many roles now include LLM-powered analytics, automated reporting, and integration with generative AI tools. The role has evolved from 'the person who runs SQL queries' to 'the person who builds AI-powered data products.'
Modern data science roles fall into two camps: analytics-focused (insights, dashboards, experimentation) and ML-focused (building predictive models, recommendation systems, NLP features). The best data scientists can operate in both modes. The AI shift means that even analytics-focused roles now involve building automated insight pipelines using LLMs, going well beyond one-off reports.
Across the 4,317 AI roles we're tracking, Data Scientist positions make up 8% of the market. At Target, this role fits into their broader AI and engineering organization.
Data Scientist roles remain in high demand, though the definition keeps shifting. Companies increasingly want candidates who can bridge traditional statistics with modern ML and LLM capabilities. The 'pure insights' data scientist role is consolidating into analytics engineering, while the 'build models' data scientist role is merging with ML engineering.
What the Work Looks Like
A typical week includes: analyzing experiment results for a product feature launch, building a predictive model for customer churn, creating an automated reporting pipeline using LLM-powered summarization, presenting insights to stakeholders, and cleaning data (always cleaning data). The ratio of analysis to engineering varies by company, but expect both.
Data Scientist roles remain in high demand, though the definition keeps shifting. Companies increasingly want candidates who can bridge traditional statistics with modern ML and LLM capabilities. The 'pure insights' data scientist role is consolidating into analytics engineering, while the 'build models' data scientist role is merging with ML engineering.
Skills Required
Python, SQL, and statistical modeling are the foundation. Increasingly, roles want experience with LLMs for data analysis, automated insight generation, and building AI-powered data products. Familiarity with cloud data platforms (Snowflake, BigQuery, Databricks) and ML frameworks (scikit-learn, PyTorch) covers most job requirements.
Experimentation design and causal inference are underrated skills that separate strong candidates. Companies care about whether their product changes cause improvements, and can distinguish causation from correlation. A/B testing methodology, Bayesian statistics, and the ability to communicate uncertainty to non-technical stakeholders are high-value skills.
Good postings specify the data stack, the types of problems you'll work on, and the team structure. Look for companies that differentiate between analytics and ML data science. Vague 'data scientist' postings that list every skill under the sun usually mean the company doesn't know what they need.
Compensation Benchmarks
Data Scientist roles pay a median of $192,890 based on 789 positions with disclosed compensation. Senior-level AI roles across all categories have a median of $227,400. Disclosed range: $132K to $238K.
Across all AI roles, the market median is $215,000. Top-quartile compensation starts at $266,300. The 90th percentile reaches $320,790. For comparison, the highest-paying categories include AI Safety ($287,500) and Research Engineer ($272,100). By seniority level: Entry: $110,000; Mid: $194,400; Senior: $227,400; Director: $274,554; VP: $241,000.
Target AI Hiring
Target has 9 open AI roles right now. They're hiring across AI/ML Engineer, Data Engineer, Data Scientist. Positions span Minneapolis, MN, US, Brooklyn Park, MN, US, Sunnyvale, CA, US. Compensation range: $135K - $303K.
Location Context
Across all AI roles, 15% (635 positions) offer remote work, while 3,657 require on-site attendance. Top AI hiring metros: New York (1,650 roles, $220,000 median); San Francisco (1,335 roles, $265,000 median); Los Angeles (708 roles, $214,112 median).
Career Path
Common paths into Data Scientist roles include Data Analyst, Statistician, Quantitative Researcher.
From here, career progression typically leads toward Senior Data Scientist, ML Engineer, AI Product Manager.
Start with statistics and SQL. Build a real analysis project on public data that demonstrates insight generation alongside model building. The market values data scientists who can communicate findings clearly to business stakeholders. If you want to move toward ML engineering, invest in software engineering fundamentals and production deployment skills.
What to Expect in Interviews
Interviews combine statistics, coding, and business acumen. SQL is almost always tested, often with complex joins and window functions. Expect a case study round where you're given a business problem and asked to design an analysis plan. Coding rounds focus on pandas, statistical modeling, and visualization. The strongest differentiator is how well you communicate insights to non-technical stakeholders during presentation rounds.
When evaluating opportunities: Good postings specify the data stack, the types of problems you'll work on, and the team structure. Look for companies that differentiate between analytics and ML data science. Vague 'data scientist' postings that list every skill under the sun usually mean the company doesn't know what they need.
AI Hiring Overview
The AI job market has 4,317 open positions tracked in our dataset. By seniority: 138 entry-level, 2,071 mid-level, 1,655 senior, and 453 leadership roles (Director, VP, C-Level). Remote roles make up 15% of the market (635 positions). The remaining 3,657 roles require on-site or hybrid attendance.
The market median for AI roles is $215,000. Top-quartile compensation starts at $266,300. The 90th percentile reaches $320,790. Highest-paying categories: AI Safety ($287,500 median, 34 roles); Research Engineer ($272,100 median, 227 roles); AI Engineering Manager ($244,000 median, 23 roles).
Data Scientist roles remain in high demand, though the definition keeps shifting. Companies increasingly want candidates who can bridge traditional statistics with modern ML and LLM capabilities. The 'pure insights' data scientist role is consolidating into analytics engineering, while the 'build models' data scientist role is merging with ML engineering.
The AI Job Market Today
The AI job market spans 4,317 open positions across 15 role categories. The largest categories by volume: AI/ML Engineer (3,004), Data Scientist (345), AI Software Engineer (309). These three account for the majority of open positions, though smaller categories often have higher per-role compensation because of specialized skill requirements.
The seniority mix tells a story about where AI teams are in their maturity. Entry-level roles (138) are outnumbered by mid-level (2,071) and senior (1,655) positions, reflecting that most companies are past the 'build a team from scratch' phase and need experienced engineers who can ship production systems. Leadership roles (Director, VP, C-Level) total 453 positions, representing the bottleneck between technical execution and organizational strategy.
Remote work availability sits at 15% of all AI roles (635 positions), with 3,657 requiring on-site or hybrid attendance. The remote share has stabilized after the post-pandemic correction. Senior and specialized roles (Research Scientist, ML Architect) are more likely to be remote-eligible than entry-level positions, partly because experienced hires have more negotiating power and partly because these roles require less hands-on mentorship.
AI compensation is structured in clear tiers. The market median sits at $215,000. Top-quartile roles start at $266,300, and the 90th percentile reaches $320,790. These figures include base salary with disclosed compensation. Total compensation (including equity, bonuses, and sign-on) runs 20-40% higher at companies that offer those components.
Category matters for compensation. AI Safety roles lead at $287,500 median, while Prompt Engineer roles sit at $145,000. The spread between highest and lowest-paying categories reflects the premium on specialized technical skills versus broader analytical roles.
The most in-demand skills across all AI postings: Python (2,249 postings), Aws (1,224 postings), Azure (938 postings), Rag (915 postings), Gcp (660 postings), Pytorch (640 postings), Prompt Engineering (624 postings), Kubernetes (559 postings). Python dominates, appearing in the vast majority of role descriptions regardless of category. Cloud platform experience (AWS, GCP, Azure) is the second most common requirement. The newer entrants to the top skills list (RAG, vector databases, LLM APIs) reflect the shift from traditional ML toward generative AI applications.
Frequently Asked Questions
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