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About This Role
Job Profile:
Business and Data Analyst 3
Job Family:
Business and Data Analytics
Time Type:
Full time
Max Pay – Depends on experience:
$114,000\.00 USD Annual
Apply before 11:59 PM Arizona time the day before the posted End Date.
Minimum Qualifications:
Bachelor's degree and five (5\) years of experience appropriate to the area of assignment/field; OR, Any equivalent combination of experience and/or training from which comparable knowledge, skills and abilities have been achieved.
Job Profile Summary:
Defines systems requirements, makes recommendations for technology selection, and performs moderately complex data analysis to ensure data management objectives within a work unit are met.
Job Description:
Supports ASU’s Actionable Analytics initiatives through the application of advanced data science, machine learning, statistical analysis, and artificial intelligence techniques, including the use of large language models (LLMs) and other foundation model technologies. Serves as a senior\-level contributor responsible for designing, developing, validating, deploying, and monitoring analytical and AI\-enabled solutions that address operational and strategic organizational needs.
Position Salary Range:
- $90,000 \- $114,000 per year, DOE
Essential Duties:
- Leverages machine learning, artificial intelligence, large language models (LLMs), and other advanced analytical techniques to develop innovative solutions that address operational and strategic institutional needs.
- Promotes the responsible, ethical, secure, and compliant use of data science, machine learning, and artificial intelligence technologies in accordance with institutional policies and applicable regulations.
- Determines and develops advanced research design methodologies, data analysis approaches, statistical modeling procedures, machine learning solutions, and generative AI applications to meet operational and strategic organizational needs.
- As part of a team, conducts all phases of analytics, machine learning, and AI solution development, including data acquisition and extraction, data cleaning, data exploration, feature engineering, prompt engineering, model development, model validation/testing deployment, monitoring, evaluation, and ongoing support.
- Selects and applies advanced statistical, predictive modeling, machine learning, large language model (LLM), and generative AI techniques to generate actionable insights and address complex organizational challenges using structured and unstructured data sources.
- Develops, deploys, integrates, and maintains statistical models, machine learning models, and AI\-enabled solutions, including applications leveraging large language models (LLMs), foundation models, and retrieval\-augmented generation (RAG) techniques in development and production environments.
- Implements monitoring and evaluation processes for statistical models, machine learning models, and AI\-enabled solutions to assess performance and enable continuous improvement.
- Collaborates with technical and non\-technical stakeholders to translate business needs into analytical solutions and actionable recommendations.
- Creates and maintains technical documentation, analytical workflows, and model documentation.
- Supports the adoption of machine learning operationalization (MLOps/AIOps) practices, including version control, model deployment, workflow automation, and reproducibility.
- Learns, evaluates, and adopts new analytical methodologies, technologies, and tools to meet changing organizational needs.
- Presents analytical findings and recommendations through written reports, presentations, and data visualizations to technical and non\-technical stakeholders.
- Provides technical guidance and mentorship to junior staff members and project teams as appropriate.
Desired Qualifications:
- Demonstrated knowledge of and experience applying generative AI and large language model (LLM) technologies, using a *wide\-ranging* suite of structured and unstructured data sources and success outcomes, as well as advanced traditional and modern/machine learning predictive modeling methodologies.
- Experience conducting all phases of analytics, machine learning, and AI solution development, including data acquisition and extraction, data cleaning, data exploration, feature engineering, prompt engineering, model development, model validation/testing, deployment, monitoring, evaluation, and ongoing support.
- Experience developing, validating, deploying, and maintaining machine learning models and AI\-enabled applications, including solutions leveraging large language models (LLMs), foundation models, and retrieval\-augmented generation (RAG) techniques in cloud or production environments.
- Demonstrated knowledge of machine learning and AI best practices, including cross\-validation, hyperparameter tuning, prompt optimization, model monitoring, performance evaluation, explainability, and responsible AI principles.
- Experience evaluating, selecting, and applying large language models (LLMs), foundation models, and generative AI technologies to support knowledge discovery, workflow automation, decision support, and business process improvement.
- Proficiency in Python and SQL, including experience leveraging libraries, frameworks, and APIs used for machine learning, data science, and generative AI applications.
- Demonstrated working knowledge of higher education data systems, including specialized data mining, natural language processing, knowledge discovery, and data visualization techniques.
- Experience using cloud\-based analytics, machine learning, and AI services to develop, deploy, monitor, and support analytical and AI\-enabled solutions.
- Experience using version control systems and collaborative development tools.
- Experience using data visualization tools and techniques to communicate complex analytical findings and insights.
- Demonstrated ability to clearly and accurately summarize findings and recommendations to technical and non\-technical stakeholders to inform decision making.
- Demonstrated ability to lead complex analytical projects, prioritize competing demands, and complete projects on time and within scope.
- Demonstrated ability to work effectively both independently and collaboratively as part of a team.
- Demonstrated ability to translate stakeholder needs into appropriate, functional, and informative analytical and AI\-enabled solutions.
- Experience mentoring or providing technical guidance to analysts, data scientists, or project teams.
Working Environment:
- Activities are performed in an environmentally controlled office setting subject to extended periods of staying in a stationary position, manipulating a computer 75 percent; required to traverse moderate distances to perform work 10 percent. Ability to clearly express oneself and effectively exchange information to perform essential functions. Frequent moving, transporting, and positioning up to 25 pounds 15 percent. Regular activities require ability to quickly change priorities, which may include or are subject to resolution of conflicts.
Department Statement:
Actionable Analytics, within the Office of the University Provost, advances student success by developing and supporting innovative enterprise analytics applications and data solutions for the Academic Enterprise. The department provides trusted data, data science solutions, actionable insights, and decision support tools that empower student success initiatives for ASU campus\-immersion and digital\-immersion students.
Driving Requirement:
Driving is not required for this position.
Location:
Campus: Tempe
Funding:
No Federal Funding
Instructions to Apply:
Current employees, student workers seeking staff opportunities, and students applying for student worker positions must apply directly through the Workday Jobs Hub.
Please use the link below to log in using single sign\-on.
https://www.myworkday.com/asu/d/inst/1$9925/9925$23236\.htmld
To be considered, your application must include all of the following attachments:
- Cover letter
- Resume or CV
Multiple documents may be uploaded in the attachments section. Alternatively, applicants may combine all required materials into a single PDF for submission. Please ensure uploaded documents are clearly labeled and include your name.
Please ensure your resume includes all employment information in month and year format, for example 6/04 to 8/14, along with job title, job duties, and employer name for each position. Your resume should clearly demonstrate how your experience and background meet the minimum and desired qualifications for this position. Incomplete applications or missing required materials may not be considered.
Important: Do not withdraw your application to make edits. Once an application is withdrawn, it cannot be edited, reactivated, or replaced with a new submission. If you have questions or need assistance, please contact The Office of Human Resources Talent Acquisition before the posting close date.
Graduate Assistant, Intern and part\-time positions are counted as half time for experience equivalency, meaning one year equals six months of experience.
Only electronic applications will be accepted for this position. By submitting an application, you confirm that the information provided is accurate and complete.
ASU Statement:
Arizona State University is a new model for American higher education, an unprecedented combination of academic excellence, entrepreneurial energy and broad access. This New American University is a single, unified institution comprising four differentiated campuses positively impacting the economic, social, cultural and environmental health of the communities it serves. Its research is inspired by real world application blurring the boundaries that traditionally separate academic disciplines. ASU serves more than 100,000 students in metropolitan Phoenix, Arizona, the nation's fifth largest city. ASU champions inclusive excellence, and welcomes students from all fifty states and more than one hundred nations across the globe.
ASU is a tobacco\-free university. For details visit https://wellness.asu.edu/explore\-wellness/body/alcohol\-and\-drugs/tobacco
All qualified applicants will receive consideration for employment without regard to race, color, religion, sex, national origin, disability, protected veteran status, or any other basis protected by law.
Notice of Availability of the ASU Annual Security and Fire Safety Report:
In compliance with federal law, ASU prepares an annual report on campus security and fire safety programs and resources. ASU’s Annual Security and Fire Safety Report is available online at https://www.asu.edu/police/PDFs/ASU\-Clery\-Report.pdf . You may request a hard copy of the report by contacting the ASU Police Department at 480\-965\-3456\.
Relocation Assistance – For information about schools, housing child resources, neighborhoods, hospitals, community events, and taxes, visit https://cfo.asu.edu/az\-resources .
Employment Verification Statement:
ASU conducts pre\-employment screening which may include verification of work history, academic credentials, licenses, and certifications.
Background Check Statement:
ASU conducts pre\-employment screening for all positions which includes a criminal background check, verification of work history, academic credentials, licenses, and certifications. Employment is contingent upon successful passing of the background check.
Fingerprint Check Statement:
This position is considered safety/security sensitive and will include a fingerprint check. Employment is contingent upon successful passing of the fingerprint check.
Salary Context
This $90K-$114K range is in the lower quartile for Data Scientist roles in our dataset (median: $157K across 236 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 3,823 AI roles we're tracking, Data Scientist positions make up 8% of the market. At Arizona State 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 $198,000 based on 808 positions with disclosed compensation. Senior-level AI roles across all categories have a median of $227,400. This role's midpoint ($102K) sits 48% below the category median. Disclosed range: $90K to $114K.
Across all AI roles, the market median is $200,100. Top-quartile compensation starts at $253,500. The 90th percentile reaches $307,500. For comparison, the highest-paying categories include AI Engineering Manager ($275,000) and AI Safety ($274,200). By seniority level: Entry: $97,880; Mid: $165,000; Senior: $227,400; Director: $247,800; VP: $250,000.
Arizona State University AI Hiring
Arizona State University has 1 open AI role right now. They're hiring across Data Scientist. Based in Tempe, AZ, US. Compensation range: $114K - $114K.
Location Context
Across all AI roles, 15% (590 positions) offer remote work, while 3,217 require on-site attendance. Top AI hiring metros: New York (2,643 roles, $211,000 median); San Francisco (2,168 roles, $253,000 median); Los Angeles (1,792 roles, $191,580 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 3,823 open positions tracked in our dataset. By seniority: 112 entry-level, 1,798 mid-level, 1,516 senior, and 397 leadership roles (Director, VP, C-Level). Remote roles make up 15% of the market (590 positions). The remaining 3,217 roles require on-site or hybrid attendance.
The market median for AI roles is $200,100. Top-quartile compensation starts at $253,500. The 90th percentile reaches $307,500. Highest-paying categories: AI Engineering Manager ($275,000 median, 41 roles); AI Safety ($274,200 median, 55 roles); Research Engineer ($260,000 median, 434 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 3,823 open positions across 15 role categories. The largest categories by volume: AI/ML Engineer (2,629), Data Scientist (322), AI Software Engineer (279). 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 (112) are outnumbered by mid-level (1,798) and senior (1,516) 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 397 positions, representing the bottleneck between technical execution and organizational strategy.
Remote work availability sits at 15% of all AI roles (590 positions), with 3,217 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 $200,100. Top-quartile roles start at $253,500, and the 90th percentile reaches $307,500. 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 Engineering Manager roles lead at $275,000 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 (1,979 postings), Aws (1,190 postings), Azure (899 postings), Rag (839 postings), Gcp (726 postings), Pytorch (595 postings), Prompt Engineering (595 postings), Claude (540 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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