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About This Role
JOB SUMMARY:
NORC at the University of Chicago is seeking a qualified Data Scientist I to join the Methodology and Quantitative Social Sciences department and support innovative research, analytics, and AI initiatives.
The Data Scientist I works collaboratively with methodologists, researchers, statisticians, software developers, and subject matter experts to develop data science and artificial intelligence solutions that improve research, operational efficiency, and decision\-making. This role combines strong foundations in data management, statistical modeling, machine learning, and computational social science with emerging capabilities in generative AI and large language models (LLMs).
The Data Scientist I will contribute to projects involving structured and unstructured data, survey data, administrative records, commercial data, text data, and other novel data sources. Responsibilities may include developing analytical workflows, building machine learning models, creating AI\-powered applications, fine\-tuning foundation models, implementing retrieval\-augmented generation (RAG) systems, and supporting deployment of AI solutions in secure cloud environments.
The ideal candidate possesses strong Python programming skills, experience building reproducible analytical workflows, and a demonstrated interest in applying modern AI technologies to solve complex research and business problems.
Location: This is a hybrid role based in our Washington, DC and Chicago downtown offices, with a minimum of six days per month in the office.
*Qualified applicants must be eligible to work in the U.S. We regret that we are unable to offer visa sponsorship for this position.*
DEPARTMENT: Methodology and Quantitative Social Sciences
The Methodology and Quantitative Social Sciences department implements state\-of\-the\-art methodologies and develops innovations to deliver reliable data and rigorous analysis to guide critical programmatic, business and policy decisions for NORC clients. The department provides leadership throughout the project lifecycle on study design, data collection, assessment of data quality, quantitative analysis, and dissemination of results. The Methodological and Quantitative Social Sciences department also conducts its own research and is a leader in designing and implementing rigorous, efficient methods for gathering, evaluating, and analyzing data from primary and secondary sources. The department provides expertise and leads NORC strategy on the use of a broad range of methods and techniques, including research and experimental design, recruitment and retention, instrument design and testing, assessing data quality, evaluating measurement properties of new measures, causal inference methods, machine learning, analysis of clustered data, data visualization, use of novel data sources and technologies to improve data gathering, and building AI solutions that support NORC’s research. The department collaborates with the Statistics and Data Science Department on areas of synergy and intersection and with all NORC subject matter departments, in addition to leading its own projects.
RESPONSIBILITIES:
- Collaborate with methodologists and subject matter experts to design, develop, evaluate, and deploy AI\-enabled applications that support research and operational objectives.
- Fine\-tune, adapt, or customize machine learning and language models for domain\-specific tasks.
- Build and evaluate retrieval\-augmented generation (RAG) solutions using vector databases and semantic search techniques for data and analysis projects in NORC’s research focus areas.
- Develop prompt engineering strategies and evaluation frameworks for generative AI systems.
- Implement model monitoring, testing, validation, and performance optimization processes.
- Apply natural language processing techniques for text classification, information extraction, summarization, and content analysis.
- Support experimentation with AI agents, tool calling, and workflow automation.
- Perform other duties as assigned.
REQUIRED SKILLS:
- Bachelor's degree in computational social science, data science, computer science or a related quantitative field with social science emphasis.
- At least 4 years of relevant experience (graduate research, internships, and applied professional experience may be considered).
- Advanced proficiency in Python.
- Strong experience with SQL and relational databases.
- Experience developing software, data pipelines, or analytical applications.
- Experience conducting statistical analysis and machine learning using real\-world datasets.
- Knowledge of supervised and unsupervised machine learning methods.
- Experience working with Git and collaborative development workflows.
- Strong problem\-solving and analytical skills.
- Excellent communication and technical writing skills.
- Ability to explain technical concepts to diverse audiences.
Preferred Skills:
- Familiarity with AI agents and workflow orchestration frameworks.
- Additional expertise in large and small language models, and machine learning, in working in cloud environments (e.g., AWS, Azure, GCP), and with command\-line workflows (e.g., in bash).
- Developing machine learning models, data pipelines, and AI\-powered applications, as described above, to support social science research.
*Qualified applicants must be eligible to work in the U.S. We regret that we are unable to offer visa sponsorship for this position.*
SALARY AND BENEFITS:
The pay range for this position is $90,000\-$100,000\.
This position is classified as regular. Regular staff are eligible for NORC’s comprehensive benefits program. Benefits include, but are not limited to:
- Generously subsidized health insurance, effective on the first day of employment
- Dental and vision insurance
- A defined contribution retirement program, along with a separate voluntary 403(b) retirement program
- Group life insurance, long\-term and short\-term disability insurance
- Benefits that promote work/life balance, including generous paid time off, holidays; paid parental leave, bereavement leave, tuition assistance, and an Employee Assistance Program (EAP).
*NORC is committed to equity and transparency in its pay practices. We publish salary ranges and benefit information for every job. The listed hiring range reflects what we, in good faith, expect to pay at the time of posting, though actual compensation may vary and may be adjusted over time. A candidate’s placement within the range depends on factors such as competencies, education, qualifications, experience, skills, performance, and organizational needs.*
WHAT WE DO:
NORC at the University of Chicago is an objective, non\-partisan research institution that delivers reliable data and rigorous analysis to guide critical programmatic, business, and policy decisions. Since 1941, our teams have conducted groundbreaking studies, created and applied innovative methods and tools, and advanced principles of scientific integrity and collaboration. Today, government, corporate, and nonprofit clients around the world partner with us to transform increasingly complex information into useful knowledge.
WHO WE ARE:
For over 80 years, NORC has evolved in many ways, moving the needle with research methods, technical applications and groundbreaking research findings. But our tradition of excellence, passion for innovation, and commitment to collegiality have remained constant components of who we are as a brand, and who each of us is as a member of the NORC team. With world\-class benefits, a business casual environment, and an emphasis on continuous learning, NORC is a place where people join for the stellar research and analysis work for which we’re known, and stay for the relationships they form with their colleagues who take pride in the impact their work is making on a global scale.
EEO STATEMENT:
NORC is an equal opportunity employer. NORC evaluates qualified applicants without regard to race, color, religion, sex, gender, national origin, disability, status as a protected veteran, sexual orientation, and other legally protected characteristics.
\#LI\-MS1
Salary Context
This $90K-$100K range is in the lower quartile for Data Scientist roles in our dataset (median: $155K across 226 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 3,708 AI roles we're tracking, Data Scientist positions make up 8% of the market. At NORC at the University of Chicago, 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 463 positions with disclosed compensation. Mid-level AI roles across all categories have a median of $200,000. This role's midpoint ($95K) sits 51% below the category median. Disclosed range: $90K to $100K.
Across all AI roles, the market median is $217,500. Top-quartile compensation starts at $272,100. The 90th percentile reaches $325,000. For comparison, the highest-paying categories include AI Safety ($300,000) and Research Engineer ($280,000). By seniority level: Entry: $120,000; Mid: $200,000; Senior: $230,000; Director: $272,150; VP: $250,000.
NORC at the University of Chicago AI Hiring
NORC at the University of Chicago has 1 open AI role right now. They're hiring across Data Scientist. Based in Chicago, IL, US. Compensation range: $100K - $100K.
Location Context
AI roles in Chicago pay a median of $205,100 across 97 tracked positions. That's 6% below the national 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 3,708 open positions tracked in our dataset. By seniority: 102 entry-level, 1,705 mid-level, 1,469 senior, and 432 leadership roles (Director, VP, C-Level). Remote roles make up 14% of the market (508 positions). The remaining 3,180 roles require on-site or hybrid attendance.
The market median for AI roles is $217,500. Top-quartile compensation starts at $272,100. The 90th percentile reaches $325,000. Highest-paying categories: AI Safety ($300,000 median, 21 roles); Research Engineer ($280,000 median, 147 roles); AI Architect ($254,798 median, 67 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 3,708 open positions across 16 role categories. The largest categories by volume: AI/ML Engineer (2,605), Data Scientist (310), AI Software Engineer (259). 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 (102) are outnumbered by mid-level (1,705) and senior (1,469) 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 432 positions, representing the bottleneck between technical execution and organizational strategy.
Remote work availability sits at 14% of all AI roles (508 positions), with 3,180 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 $217,500. Top-quartile roles start at $272,100, and the 90th percentile reaches $325,000. 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 $300,000 median, while Prompt Engineer roles sit at $140,000. 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 (1,890 postings), Aws (1,103 postings), Azure (877 postings), Rag (855 postings), Gcp (631 postings), Prompt Engineering (560 postings), Pytorch (545 postings), Claude (498 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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