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Data Science Student Jobs in Colorado (NOW HIRING)

Analytics Engineering Intern

Denver, CO

$17.25 - $22.50/hr

This role is ideal for students with curiosity for programming, process automation, and building ... Data Science, Computer Science, or related fields. * Interest in data analysis, business ...

VP, Regional Digital Analytics

Denver, CO · On-site

  • Medical

  • Dental

  • Life

  • Retirement

  • PTO

Bachelor's degreerequired; advanced degree in analytics, data science, economics, statistics ... Student Loan Repayment (below manager level only) * Parental Leave * One day volunteer time off ...

Analytics Engineering Intern

Denver, CO · On-site +1

$17.25 - $22.50/hr

This role is ideal for students with curiosity for programming, process automation, and building ... Data Science, Computer Science, or related fields. * Interest in data analysis, business ...

Analytics Engineering Intern

Denver, CO

$17.25 - $22.50/hr

This role is ideal for students with curiosity for programming, process automation, and building ... Data Science, Computer Science, or related fields. * Interest in data analysis, business ...

Showing results 41-60

Data Science Student information

See Colorado salary details

$39.4K

$129.1K

$206.6K

How much do data science student jobs pay per year?

As of Aug 14, 2026, the average yearly pay for data science student in Colorado is $129,062.00, according to ZipRecruiter salary data. Most workers in this role earn between $103,600.00 and $143,000.00 per year, depending on experience, location, and employer.

What types of projects or hands-on experiences can I expect as a data science student?

As a Data Science Student, you can expect to work on a variety of data-driven projects such as analyzing real-world datasets, building predictive models, and creating data visualizations. These projects often involve using popular programming languages and tools to solve practical problems, either individually or as part of a team. Many programs encourage participation in hackathons, internships, or collaborative research to provide hands-on experience and deeper industry exposure. Gaining experience through these projects is crucial for building your portfolio and preparing for future roles in the data science field.

What jobs can you get if you study data science?

Data science graduates can pursue roles such as data analyst, data scientist, machine learning engineer, business intelligence analyst, and data engineer. These positions typically require skills in programming, statistical analysis, and data visualization tools like Python, R, SQL, and Tableau.

What are the key skills and qualifications needed to thrive as a data science student, and why are they important?

To thrive as a Data Science Student, a solid understanding of statistics, programming (especially in Python or R), and data analysis concepts is required, often backed by enrollment in a relevant degree or certification program. Familiarity with tools such as Jupyter Notebook, SQL, and visualization libraries, as well as participation in online courses or bootcamps, is highly valuable. Strong problem-solving abilities, curiosity, and effective communication skills help students excel when tackling projects and collaborating with peers. These skills are essential for mastering complex concepts, contributing to team projects, and preparing for a successful transition into a data science career.

What is a data science student?

A Data Science Student job typically refers to an internship, part-time role, or research position where students apply their data science skills in a practical setting. These roles often involve working with real-world datasets, building models, analyzing trends, and assisting in data-driven decision-making. Students may use programming languages like Python or R, work with machine learning algorithms, and gain experience with tools such as SQL, Pandas, and TensorFlow. This hands-on experience helps bridge the gap between academic learning and industry applications, preparing students for full-time roles after graduation.

What are popular job titles related to Data Science Student jobs in Colorado?

For Data Science Student jobs in Colorado, the most frequently searched job titles are:

Infographic showing various Data Science Student job openings in Colorado as of August 2026, with employment types broken down into 1% As Needed, 84% Full Time, 12% Part Time, and 3% Contract. Highlights an 86% Physical, 4% Hybrid, and 10% Remote job distribution, with an average salary of $129,062 per year, or $62 per hour.

Open Rank - Professor of Biostatistics

University of Northern Colorado

Greeley, CO

Full-time

Re-posted 23 days ago


Job description

To ensure full consideration, applications must be received by 11:59pm (MT) on 05/17/2026.

Position Summary:

UNC COM seeks a collaborative, service-oriented faculty Biostatistician at the rank of Assistant, Associate, or Full Professor to provide high-quality statistical and data-science expertise supporting the College's curriculum, faculty scholarship, and student outcomes, while also serving as a key contributor to accreditation analytics and institutional reporting. In this role, the Biostatistician will partner with Academic Affairs, assessment and curriculum leadership, faculty investigators, and student support teams to design and implement analytic strategies, ensure data integrity and governance, and translate findings into actionable insights that advance continuous quality improvement and strengthen compliance readiness.

Job Duties:

Academic Assessment, Curriculum & Learner Analytics
o   Design and implement analytic plans for course and program evaluation, including exam/item analysis, outcomes attainment, and longitudinal performance tracking across the preclinical and clinical curriculum.
o   Develop repeatable reporting processes and dashboards for curriculum committees and course directors (e.g., trend analyses, cohort comparisons, reliability/validity indicators, learning outcomes mapping analytics).
o   Support continuous quality improvement (CQI) by identifying data signals, quantifying impact of curricular changes, and recommending measurement strategies aligned to program outcomes.

Faculty Teaching & Educational Support Responsibilities
o   Teach and/or co-teach assigned content within the UNC COM curriculum, with primary focus on biostatistics, epidemiology, population health, and public/public health concepts (e.g., study design, probability, inference, regression, screening/test characteristics, bias/confounding, and interpretation of medical literature).
o   Develop and deliver learning activities (lectures, small groups, team-based learning, case-based sessions, workshops) that integrate statistical reasoning into clinical and population health decision-making.
o   Create and refine assessment items (quizzes, exams, OSCE/CPX adjuncts, assignments) aligned with course objectives and program competencies; contribute to blueprinting and standard-setting activities as needed.
o   Collaborate with course directors and curriculum committees to align biostatistics and population/public health content vertically and horizontally across the curriculum, ensuring appropriate sequencing, integration, and competency progression.

Accreditation Data, Compliance Reporting & CQI Infrastructure
o   Serve as a primary analytic resource for accreditation-related data requests and reporting (e.g., annual updates, self-study exhibits, evidence tables, outcomes dashboards), ensuring accuracy, traceability, and documentation.
o   Build and maintain an "accreditation evidence" data pipeline: standardized definitions, data dictionaries, audit trails, version control, and clear source of all submitted metrics.
o   Coordinate with institutional partners (Institutional Research, Registrar, IT, Student Affairs, Finance, HR) to compile, validate, and reconcile datasets used in accreditation and internal governance.

Faculty & Student Research Support (Consultation Model)
o   Provide statistical consultation to faculty and students for scholarly projects (study design, power/sample size, data management plans, modeling strategies, interpretation, and reporting).
o   Assist with IRB- and protocol-related analytic sections, including statistical methods narratives for manuscripts, abstracts, and grant submissions.
o   Promote reproducible research practices (analysis plans, code review, documentation standards, and appropriate use of statistical software).

Data Governance, Quality, and Operational Excellence
o   Establish and enforce analytic standards (naming conventions, reproducible workflows, secure data handling, and role-based access).
o   Create user-friendly templates (analysis request intake forms, study analysis plans, reporting calendars, and standard output formats).
o   Communicate findings effectively to non-technical stakeholders via concise narratives, visualizations, and executive summaries.

Minimum Qualifications:

o   Master's degree (or higher) in Biostatistics, Statistics, Data Science, Epidemiology, or a closely related quantitative discipline.
o   Demonstrated experience conducting applied statistical analyses in academic health sciences, higher education assessment, healthcare, or related settings.
o   Proficiency with statistical programming and best practices in reproducible analytics.
o   Demonstrated ability to translate complex analyses into clear, decision-relevant conclusions for diverse stakeholders.
o   Strong interpersonal skills; ability to manage multiple concurrent projects with appropriate prioritization and customer-service orientation.

Preferred Qualifications
o   Experience supporting accreditation, regulatory reporting, outcomes assessment, or CQI analytics (medical education experience strongly preferred).
o   Experience with psychometrics/educational measurement.
o   Experience designing dashboards and data pipelines using business intelligence tools.
o   Familiarity with learning management system (LMS) data, survey platforms, and student information systems; ability to integrate multi-source datasets.
o   Publication record and/or demonstrated history of collaborative scholarship and statistical consultation.