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Details
Open Date 07/16/2026
Requisition Number PRN45737B
Job Title Data Scientists
Working Title Data Scientists
Career Progression Track P00
Track Level P3 \- Career, P2 \- Developing, P1 \- Entry Level Pro
FLSA Code Computer Employee
Patient Sensitive Job Code? No
Standard Hours per Week 40
Full Time or Part Time? Full Time
Shift Day
Work Schedule Summary
VP Area U of U Health \- Academics
Department 00847 \- Hematology
Location Campus
City Salt Lake City, UT
Type of Recruitment External Posting
Pay Rate Range 75,000 \- 105,000
Close Date 10/31/2026
Priority Review Date (Note \- Posting may close at any time) 07/16/2026
Job Summary
Data Scientists
Produce innovative solutions driven by exploratory data analysis from complex and high\-dimensional datasets. Apply knowledge of statistics, machine learning, programming, data modeling, simulation, and advanced mathematics to recognize patterns, identify opportunities, pose business questions, and make valuable discoveries leading to prototype development and product improvement. Use a flexible, analytical approach to design, develop, and evaluate predictive models and advanced algorithms that lead to optimal value extraction from the data. Generate and test hypotheses and analyze and interpret the results of product experiments. Work with product engineers to translate prototypes into new products, services, and features and provide guidelines for large\-scale implementation.
Learn more about the great benefits of working for University of Utah: benefits.utah.edu
The department may choose to hire at any of the below job levels and associated pay rates based on their business need and budget.
Responsibilities
Data Scientist, I
Produce innovative solutions driven by exploratory data analysis from complex and high\-dimensional datasets. Apply knowledge of statistics, machine learning, programming, data modeling, simulation, and advanced mathematics to recognize patterns, identify opportunities, pose business questions, and make valuable discoveries leading to prototype development and product improvement. Use a flexible, analytical approach to design, develop, and evaluate predictive models and advanced algorithms that lead to optimal value extraction from the data. Generate and test hypotheses and analyze and interpret the results of product experiments. Work with product engineers to translate prototypes into new products, services, and features and provide guidelines for large\-scale implementation. Requires basic skill set and proficiency. Conducts work assignments as directed. Closely supervised with little latitude for independent judgment.
Requires a bachelor’s (or equivalency) \+ 2 years of directly related work experience or a master’s (or equivalency) degree.
This is an Entry\-Level position in the General Professional track.
Job Code: P33861
Grade: P16
Data Scientist, II
Produce innovative solutions driven by exploratory data analysis from complex and high\-dimensional datasets. Apply knowledge of statistics, machine learning, programming, data modeling, simulation, and advanced mathematics to recognize patterns, identify opportunities, pose business questions, and make valuable discoveries leading to prototype development and product improvement. Use a flexible, analytical approach to design, develop, and evaluate predictive models and advanced algorithms that lead to optimal value extraction from the data. Generate and test hypotheses and analyze and interpret the results of product experiments. Work with product engineers to translate prototypes into new products, services, and features and provide guidelines for large\-scale implementation. Requires moderate skill set and proficiency in discipline. Conducts work assignments of increasing complexity, under moderate supervision with some latitude for independent judgment.
Requires a bachelor’s (or equivalency) \+ 4 years or a master’s (or equivalency) \+ 2 years of directly related work experience.
This is a Developing\-Level position in the General Professional track.
Job Code: P33862
Grade: P18
Data Scientist, III
Produce innovative solutions driven by exploratory data analysis from complex and high\-dimensional datasets. Apply knowledge of statistics, machine learning, programming, data modeling, simulation, and advanced mathematics to recognize patterns, identify opportunities, pose business questions, and make valuable discoveries leading to prototype development and product improvement. Use a flexible, analytical approach to design, develop, and evaluate predictive models and advanced algorithms that lead to optimal value extraction from the data. Generate and test hypotheses and analyze and interpret the results of product experiments. Work with product engineers to translate prototypes into new products, services, and features and provide guidelines for large\-scale implementation. Considered highly skilled and proficient in discipline. Conducts complex, important work under minimal supervision and with wide latitude for independent judgment.
Requires a bachelor’s (or equivalency) \+ 6 years or a master’s (or equivalency) \+ 4 years of directly related work experience.
This is a Career\-Level position in the General Professional track.
Job Code: P33863
Grade: P20
Minimum Qualifications
EQUIVALENCY STATEMENT : 1 year of higher education can be substituted for 1 year of directly related work experience (Example: bachelor’s degree \= 4 years of directly related work experience).
Department may hire employee at one of the following job levels:
Data Scientist, I: Requires a bachelor’s (or equivalency) \+ 2 years of directly related work experience or a master’s (or equivalency) degree.
Data Scientist, II: Requires a bachelor’s (or equivalency) \+ 4 years or a master’s (or equivalency) \+ 2 years of directly related work experience.
Data Scientist, III : Requires a bachelor’s (or equivalency) \+ 6 years or a master’s (or equivalency) \+ 4 years of directly related work experience.
Preferences
Master’s Degree Preferred.
Type Benefited Staff
Special Instructions Summary
Additional Information
The University is a participating employer with Utah Retirement Systems (“URS”). Eligible new hires with prior URS service, may elect to enroll in URS if they make the election before they become eligible for retirement (usually the first day of work). Contact Human Resources at (801\) 581\-7447 for information. Individuals who previously retired and are receiving monthly retirement benefits from URS are subject to URS’ post\-retirement rules and restrictions. Please contact Utah Retirement Systems at (801\) 366\-7770 or (800\) 695\-4877 or University Human Resource Management at (801\) 581\-7447 if you have questions regarding the post\-retirement rules.
This position may require the successful completion of a criminal background check and/or drug screen.
The University of Utah values candidates who have experience working in settings with students and possess a strong commitment to improving access to higher education.
Veterans’ preference is extended to qualified applicants, upon request and consistent with University policy and Utah state law. Upon request, reasonable accommodations in the application process will be provided to individuals with disabilities.
Consistent with state and federal law, the University of Utah does not discriminate based upon race, ethnicity, color, religion, national origin, age, disability, sex, sexual orientation, gender, gender identity, gender expression, pregnancy, pregnancy\-related conditions, genetic information, or protected veteran’s status. The University does not discriminate on the basis of sex in the education program or activity that it operates, as required by Title IX and 34 CFR part 106\. The requirement not to discriminate in education programs or activities extends to admission and employment. Inquiries about the application of Title IX and its regulations may be referred to the Title IX Coordinator, to the Department of Education, Office for Civil Rights, or both.
To request a reasonable accommodation for a disability or if you or someone you know has experienced discrimination or sexual misconduct including sexual harassment, you may contact the Director/Title IX Coordinator in the Office of Equal Opportunity and Title IX ( OEO ). More information, including the Director/Title IX Coordinator’s office address, electronic mail address, and telephone number can be located at the: University of Utah Non‑Discrimination page .
Online reports may be submitted at https://oeo.utah.edu
\*\*https://publicsafety.utah.edu/safetyreport/\*\*This report includes statistics about criminal offenses, hate crimes, arrests and referrals for disciplinary action, and Violence Against Women Act offenses. They also provide information about safety and security\-related services offered by the University of Utah. A paper copy can be obtained by request at the Department of Public Safety located at 1658 East 500 South.
As per University of Utah policy 5\-108: Transfer of Benefits Eligible Staff Members , a new hire to the University of Utah who is still serving a 12 month probationary period will not be hired into another University of Utah job (a transfer) until the successful completion of the probationary period.
Salary Context
This $75K-$105K range is in the lower quartile 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 University of Utah, 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 in Demand for This Role
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. This role's midpoint ($90K) sits 53% below the category median. Disclosed range: $75K to $105K.
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.
University of Utah AI Hiring
University of Utah has 6 open AI roles right now. They're hiring across AI/ML Engineer, MLOps Engineer, Research Scientist, Data Scientist. Based in Salt Lake City, UT, US. Compensation range: $105K - $135K.
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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