Interested in this Data Scientist role at Zions Bancorporation?
Apply Now →Skills & Technologies
About This Role
Intern – Applied AI Scientist
2027 Paid Summer Internship
May through Mid-August
This internship is not eligible for relocation assistance. Local candidates preferred.
Zions Bancorporation's Internship and Banker Development Program positions are not eligible for employment visa sponsorship (e.g., H-1B visa). This includes candidates who may currently have temporary work authorization through programs such as CPT or OPT but would require future sponsorship to maintain employment eligibility.
About Us
Zions Bancorporation's Enterprise Data & Analytics (EDA) team is transforming how data, analytics, and artificial intelligence drive business outcomes across the organization. We operate in a fast-paced, data-driven environment where curiosity, innovation, and continuous improvement are highly valued.
Our mission is to deliver simple, scalable, and impactful solutions that improve customer experiences, reduce operational friction, mitigate risk, and enable better decision making. We believe that improving the work is the work, and we empower our team members to experiment, learn, and build innovative solutions using modern technologies.
EDA provides opportunities to grow both technically and professionally through mentorship, networking, community involvement, and participation in initiatives that foster collaboration, inclusion, and continuous learning. We invest in our people because they are our greatest differentiator.
Position Summary
As an Applied AI Scientist Intern, you will work alongside Data Scientists, AI Engineers, and business partners to solve real-world problems using data science, machine learning, and generative AI technologies.
You will gain hands-on experience building analytical models, developing AI-powered applications, exploring large language models (LLMs), and working with modern cloud-based data platforms. This role is ideal for students who are passionate about applying AI and data science to create measurable business value.
Responsibilities
Partner with Data Scientists, AI Engineers, and business stakeholders to understand business challenges and identify opportunities for AI and data-driven solutions.
Explore, prepare, clean, and analyze structured and unstructured datasets.
Develop, evaluate, and improve statistical, machine learning, and AI models.
Build prototypes and proof-of-concepts that demonstrate business value.
Create visualizations, reports, and presentations that effectively communicate findings and recommendations.
Assist in the development of predictive, prescriptive, and generative AI solutions.
Contribute to applications utilizing large language models (LLMs), retrieval-augmented generation (RAG), and agent-based workflows.
Experiment with modern AI engineering frameworks and model APIs.
Learn and apply software engineering best practices including version control, testing, code reviews, and deployment automation.
Collaborate with cross-functional teams to deploy and monitor AI and analytics solutions.
Stay current with emerging trends in machine learning, generative AI, and software development.
Perform other duties as assigned.
Minimum Qualifications
Currently pursuing a Bachelor's or Master's degree in Computer Science, Data Science, Statistics, Mathematics, Engineering, Artificial Intelligence, Machine Learning, or a related quantitative discipline.
Strong programming skills in Python .
Understanding of statistics, probability, hypothesis testing, regression, and data analysis methods.
Familiarity with Python data science libraries such as Pandas, NumPy, Scikit-learn, or similar tools.
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 9,653 AI roles we're tracking, Data Scientist positions make up 5% of the market. At Zions Bancorporation, 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 $190,000 based on 1,406 positions with disclosed compensation. Entry-level AI roles across all categories have a median of $110,408.
Across all AI roles, the market median is $213,400. Top-quartile compensation starts at $264,230. The 90th percentile reaches $319,400. For comparison, the highest-paying categories include AI Safety ($253,100) and Research Engineer ($252,000). By seniority level: Entry: $110,408; Mid: $193,200; Senior: $228,700; Director: $272,275; VP: $235,000.
Zions Bancorporation AI Hiring
Zions Bancorporation has 2 open AI roles right now. They're hiring across Data Scientist. Positions span Salt Lake City, UT, US, Midvale, UT.
Location Context
Across all AI roles, 6% (568 positions) offer remote work, while 9,010 require on-site attendance. Top AI hiring metros: New York (2,878 roles, $221,000 median); San Francisco (2,413 roles, $264,500 median); Los Angeles (1,340 roles, $210,000 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 9,653 open positions tracked in our dataset. By seniority: 193 entry-level, 4,500 mid-level, 3,455 senior, and 1,505 leadership roles (Director, VP, C-Level). Remote roles make up 6% of the market (568 positions). The remaining 9,010 roles require on-site or hybrid attendance.
The market median for AI roles is $213,400. Top-quartile compensation starts at $264,230. The 90th percentile reaches $319,400. Highest-paying categories: AI Safety ($253,100 median, 57 roles); Research Engineer ($252,000 median, 419 roles); AI Engineering Manager ($251,622 median, 51 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 9,653 open positions across 16 role categories. The largest categories by volume: AI/ML Engineer (7,301), Data Scientist (441), AI Software Engineer (438). 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 (193) are outnumbered by mid-level (4,500) and senior (3,455) 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 1,505 positions, representing the bottleneck between technical execution and organizational strategy.
Remote work availability sits at 6% of all AI roles (568 positions), with 9,010 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 $213,400. Top-quartile roles start at $264,230, and the 90th percentile reaches $319,400. 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 $253,100 median, while Prompt Engineer roles sit at $141,440. 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 (1,605 postings), Aws (916 postings), Azure (724 postings), Rag (634 postings), Gcp (522 postings), Pytorch (495 postings), Kubernetes (384 postings), Claude (383 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
Get Weekly AI Career Intelligence
Salary data, skills demand, and market signals from 16,000+ AI job postings. Every Monday.