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About This Role
This is for a one\-year fixed\-term position with the opportunity for extension based on performance.
The Regulation, Evaluation, and Governance Lab (RegLab) at Stanford University is looking for a full\-time Data Scientist to provide analytical expertise across our research programs.
About Us:
Stanford RegLab
builds the evidence base and technology for effective government. Our interdisciplinary team of engineers, data scientists, social scientists, and legal experts partners with agencies at every level—from federal departments to states, counties, and cities—bringing frontier AI, machine learning, and causal inference to the public sector. RegLab’s work has prompted an overhaul of tax auditing, mapped racial covenants across millions of records, and enabled streamlining of statutes and regulations.
You will:
- Work closely with the Faculty Director, Research Directors, Senior Data Scientists, and teams of fellows and students to drive forward a diverse research program focused on machine learning and policy evaluation
- Design, implement, and interpret the results of new experiments and studies.
- Work with large untapped data sets, including: health and environmental enforcement data, mass adjudication records, high\-resolution satellite imagery (15cm/pixel), and the largest publicly available corpus of legal text
- Develop and devise state\-of\-the\-art machine learning models, algorithms, and statistical models, while leading the collection of new data and the refinement of existing data sources.
- Devise methods for identifying data patterns, trends in available information sources using a variety of qualitative and quantitative techniques. Determine and recommend additional data collection and reporting requirements. Lead the implementation of data standards and common data elements for data collection.
Work with self\-initiated direction to assess and produce relevant, standard, or custom information (reports, charts, graphs and tables) from structured data sources by querying data repositories and generating the associated information. Write and distribute reports
based on data analysis
- to applicable agencies, researchers, or other internal end\-users.
- Serve as a resource for non\-routine inquiries such as requests for statistics or surveys.
Have the opportunity to
- receive co\-authorship on research papers
Preferred Qualifications
- A bachelor's degree, or MS or Ph.D., in a relevant quantitative field (e.g., data science, computer science, statistics, engineering, mathematics, economics, or a related field) and three years of (a) relevant professional experience or (b) combination of education and relevant professional experience
Expert knowledge of programming languages
- (such as Python, R, and/or SQL)
- A deep understanding of modern statistical and machine learning models, when to apply them, and how to evaluate their performance
Excellent written and verbal communication skills
- and a focus on achieving results
- Ability to work effectively with multiple internal and external customers, and ability to take a leadership role on projects and with users/clients.
- Self\-guided, self\-learner, and engaged in the mission of the Lab
Nice to Haves:
- Specialization in machine learning frameworks (TensorFlow, TF, PyTorch, Scikit Learn, etc.), NLP, computer vision, or related fields
- Academically\-minded, with experience working in an academic setting
Application Instructions:
We invite you to apply by clicking on the "Apply Now" button found on the Stanford Careers website. To be considered, please submit the following items along with your online application:
- Cover letter
- CV or resume
- Project and code samples or Github
Core Duties :
- Collect, manage and clean datasets.
- Employ new and existing tools to interpret, analyze, and visualize multivariate relationships in data.
- Create databases and reports, develop algorithms and statistical models, and perform statistical analyses appropriate to data and reporting requirements.
- Use system reports and analyses to identify potentially problematic data, make corrections, and determine root cause for data problems from input errors or inadequate field edits, and suggest possible solutions.
- Develop reports, charts, graphs and tables for use by investigators and for publication and presentation.
- Analyze data processes in documentation.
- Collaborate with faculty and research staff on data collection and analysis methods.
- Provide documentation based on audit and reporting criteria to investigators and research staff.
- Communicate with government officials, grant agencies and industry representatives.
Minimum Education :
Bachelor's degree or a combination of education and relevant experience.
Minimum Experience :
Experience in a quantitative discipline such as economics, finance, statistics or engineering.
Knowledge, Skills and Abilities :
- Substantial experience with MS Office and analytical programs.
- Strong writing and analytical skills.
- Ability to prioritize workload.
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 Stanford University, 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. Mid-level AI roles across all categories have a median of $194,400.
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.
Stanford University AI Hiring
Stanford University has 3 open AI roles right now. They're hiring across AI Software Engineer, Research Scientist, Data Scientist. Based in Stanford, CA, US. Compensation range: $199K - $199K.
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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