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About This Role
Salary Grade
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Case Western Reserve University is committed to providing a transparent estimate of the salary range available for this position at the time of its posting. The salary range is between $71,041 and $89,867, depending on qualifications, experience, department budgets, and industry data.
Employees receive more than just a paycheck. University employees enjoy a comprehensive benefits package that includes excellent healthcare, retirement plans, tuition assistance, paid time off, and a winter recess.
Job Description
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POSITION OBJECTIVE
Under limited supervision, create, test, and train data models for university business office clients. Provide university comprehensive support for artificial intelligence and machine learning models, which provide predictive and prescriptive analysis of university business performance for enhanced management of the organization. Provide advanced support for business management, university leadership, and other data end users in the form of synthesized insights drawn from statistical and visual analysis of university data assets for improved business efficiency and decision\-making at all levels of the organization.
ESSENTIAL FUNCTIONS
- Import data from desperate source systems into the enterprise data lake, creating effective, scalable data pipelines for data analytics developers and data scientists in UTech and across the organization. Design and develop programs to address requirements for data transformations and metadata documentation. Maintain a high level of data ethics managing and maintaining university data. (30%)
- Design and develop data analytics visualizations and data models for predictive and prescriptive analytics products using advanced statistical and mathematical concepts. Monitor, train, and improve data models to be as accuracy and beneficial as possible. Apply Machine Learning, Artificial Intelligence and Statistical expertise to implement analytics digital solutions that increase efficiency, reduce operational costs, and improve decision making and asset management. (30%)
- Provide technical support for data analytics applications including analyzing, troubleshooting, and resolving complex performance issues on enterprise data pipelines. Collaborate in the definition of data requirements, perform modelling and analysis, validate with the subject matter experts (SMEs), examine and identify patterns and trends to help answer business questions. (30%)
- Mentor team members to improve data analytics maturity at the organization. Coordinate and integrate assigned work with other team members. (10%)
NONESSENTIAL FUNCTIONS
Perform other duties as assigned. (\<1%)
CONTACTS
Department: Regular contact with supervisor to review goals, achievements, and overall performance. Daily contact with UTech managers and staff to address issues and opportunities collaboratively and to resolve any outstanding issues or challenges. Frequent contact with all other UTech staff to facilitate and promote joint action and cooperation to achieve results.
University: Frequent contact with Directors, Managers, Administrators, and project staff to establish a network of internal clients to serve by providing analytic tools for business operations improvements.
External: Regular contact with vendors, technology support staff, peer institutions, and others to establish a strong working knowledge of systems used for data management and data analytics.
Students: Little to no contact with students.
SUPERVISORY RESPONSIBILITIES
May supervise work of data analytics developers, business systems analysts, and/or contractors. May supervise work of project team members. Provide mentoring for data analytics developers. May ensure others comply with established standards.
QUALIFICATIONS
Bachelor's degree in one of the following preferred degrees, computer science, information technology, or related field and at least 2 to 5 years of progressive experience, OR Master's degree in one of the following fields, computer science, mathematics, statistics, data analytics, data science, or related field and at least 1 to 2 years of progressive experience.
REQUIRED SKILLS
- In\-depth knowledge of principles and methods of Data Analytics Development Life Cycle.
- In\-depth knowledge of relational database theory and data relationships.
- In\-depth knowledge of business area knowledge outside of technology at the university such as marketing, advancement, human resources, registrar/enrollment, finance, etc.
- In\-depth knowledge of statistical analysis (including, ANOVA, t\-tests, A/B testing, regression analysis, correlation analysis, etc.) and experience with statistical software platforms (i.e., R, SPSS, SAS or equivalent), and knowledge of linear algebra and calculus.
- In\-depth knowledge of how to use AI and programmatically interact with AI.
- In\-depth knowledge of at least two programming languages (Python and SQL preferred).
- In\-depth knowledge of data visualization using software platforms (Tableau preferred).
- In\-depth knowledge of data governance, access, and privacy.
- In\-depth knowledge of standard PC software packages, including word processing, spreadsheet, database, and flowcharting.
- In\-depth knowledge of extract, transform and loading techniques for cleaning data.
- In\-depth knowledge of creating, testing, and training predictive modeling and machine learning algorithms (i.e., logistic regression, decision trees, Random Forest, gradient boosting, etc.).
- In\-depth knowledge of establishing Application Programming Interfaces (APIs) for data pipelines, data mining, and interacting with AI.
- Experienced with using Databricks data platform, AWS services and cloud computing.
- In\-depth knowledge of data governance, access, and privacy knowledge.
- Excellent oral and written communication skills with the ability to communicate effectively and engage with a varied user base having varied levels of technical proficiencies. Ability to convey technical or complex information to others in non\-technical terms. Ability to interact with colleagues, supervisors and customers face to face.
- Basic project management methods, tools, and techniques (i.e., waterfall, agile, scrum).
- High level of curiosity and attention to detail.
- Strong desire to learn new things and self\-initiative.
- Ability to work efficiently and effectively with a remote or hybrid team.
- Ability to look at situations from multiple perspectives, break problems into component parts, and look for underlying causes and think through the consequences of different courses of action. Ability to identify various types of problems along with the creation of workable solutions. Requires the identification and analysis of problems, evaluation of alternatives, and provision of solutions.
- Ability to develop networks and use them to strengthen internal and external support. Ability to identify opportunities and take action to build strategic relationships between UTech and other university areas, teams, departments, etc., to help achieve business goals. Ability to work effectively at all levels within an organization.
- Ability to respond to difficult, stressful, or sensitive interpersonal situations in ways that reduce or minimize potential conflict and maintain good working relationships among internal and external customers. The ability to recognize awkward or potentially embarrassing situations that sometimes arise. Always aware of tone and careful choice of words, while at the same time ensuring that the intended message is clear, polite, and readily understood.
- Ability to develop in\-depth understanding of client needs in order to be more helpful. The ability to consider how different audiences are likely to respond and choose the best method of communicating the message to each audience.
- Ability to recognize the importance of certain tasks and responsibilities and the ability to prioritize to ensure that deadlines are met.
- Ability to be flexible in order to meet the constantly changing scope and needs of the department, division and customers being served. Ability to work in a face\-paced environment while managing multiple projects. Ability to optimize the use of time and resources to achieve the desired results; effectively plans and organizes work to minimize crises; prioritizes appropriately.
- Individual must possess the ability to manage and coordinate the activities of individuals over which the position may have no direct authority.
- Ability to properly handle sensitive, confidential, or proprietary information, data, documents, etc.
- Consistently models high standards of honesty, integrity, trust, openness, and respect for the individual. Must have the ability to use discretion and good judgment on sensitive and important matters.
- Ability to meet consistent attendance.
- Ability to demonstrate successful support, education, and advocacy for all students, aligned with the values, mission, and messaging of the university, while adhering to the staff policy on conflict of commitment and interest.
- Willingness to learn and work with artificial intelligence (AI) tools and technologies.
WORKING CONDITIONS
Professional office setting and/or remote work setting. Participation in regular status and project meetings via in\-person and video conference, team participation using online communications tools, and use of department project management tools. There are periods of stressful times especially during project deadlines. There may be occasional pressure from demanding customers. Due to time constraints, many functions must be completed on set deadlines. Travel between various locations on campus may be required. The position requires typing on a computer keyboard and using a computer mouse and a printer.
Hybrid Eligibility
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This position is eligible for hybrid work arrangement up to two remote days per week at the discretion of the department. New employees may begin a hybrid schedule upon approval from the supervisor, successful completion of an orientation period and signing the remote work checklist certification form.
EEO Statement
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Case Western Reserve University is an equal opportunity employer. All applicants are protected under federal and state laws and university policy from discrimination based on race, color, religion, sex, sexual orientation, gender identity or expression, national or ethnic origin, protected veteran status, disability, age and genetic information.
Reasonable Accommodations
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Case Western Reserve University complies with the Americans with Disabilities Act regarding reasonable accommodations for applicants with disabilities. Applicants requiring a reasonable accommodation for any part of the application and hiring process should contact the CWRU Office of Equity at 216\-368\-3066 to request a reasonable accommodation. Determinations as to granting reasonable accommodations for any applicants will be made on a case\-by\-case basis.
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Salary Context
This $71K-$89K 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 Case Western Reserve 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. This role's midpoint ($80K) sits 58% below the category median. Disclosed range: $71K to $89K.
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.
Case Western Reserve University AI Hiring
Case Western Reserve University has 1 open AI role right now. They're hiring across Data Scientist. Based in Cleveland, OH, US. Compensation range: $89K - $89K.
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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