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How much do data science fellowship jobs pay per year?

As of Sep 5, 2026, the average yearly pay for data science fellowship in the United States is $122,738.00, according to ZipRecruiter salary data. Most workers in this role earn between $98,500.00 and $136,000.00 per year, depending on experience, location, and employer.

What is a data science fellowship?

A Data Science Fellowship is an intensive training program designed to help individuals develop advanced skills in data science, analytics, and machine learning. These fellowships typically combine hands-on projects, mentorship, and networking opportunities to prepare participants for careers in data science. They often cater to recent graduates, career changers, or professionals seeking specialized experience, and may last anywhere from a few weeks to several months. Many programs also offer real-world experience by partnering with companies for project-based learning.

What types of projects and collaboration opportunities can I expect during a data science fellowship?

During a Data Science Fellowship, you can expect to work on real-world, data-driven projects that tackle challenges faced by partner organizations or sponsors. Fellows often collaborate closely with multidisciplinary teams, including other data scientists, domain experts, and sometimes clients, to develop actionable solutions. This environment fosters learning through peer feedback and mentorship, while providing the chance to build a diverse portfolio. Regular meetings, code reviews, and presentations are common, helping fellows develop both technical and communication skills essential for career advancement.

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

To thrive as a Data Science Fellow, you need a strong background in statistics, programming (often in Python or R), and data analysis, usually supported by an advanced degree in a quantitative field. Familiarity with machine learning libraries, data visualization tools, and version control systems is typically required, along with experience using platforms like Jupyter or SQL databases. Critical thinking, problem-solving, and the ability to communicate complex findings clearly are essential soft skills for this role. These skills and qualities are vital for extracting actionable insights from data and effectively collaborating with multidisciplinary teams.
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Data Science Fellowship jobs near you
Infographic showing various Data Science Fellowship job openings in the United States as of August 2026, with employment types broken down into 1% As Needed, 85% Full Time, 11% Part Time, and 3% Contract. Highlights an 86% Physical, 3% Hybrid, and 11% Remote job distribution, with an average salary of $122,738 per year, or $59 per hour.

Post Doctoral Fellow - PHS/Data Science

Clemson University

Clemson, SC • On-site

$65K/yr

Full-time, Contractor

Re-posted 11 days ago

Clemson University rating

7.0

Company rating: 7.0 out of 10

Based on 25 frontline employees who took The Breakroom Quiz

442nd of 630 rated colleges and universities

Job description

Description
POSTDOCTORAL FELLOW IN DATA SCIENCE: OUTBREAK ANALYTICS AND DISEASE MODELING
Title: Postdoctoral Fellow
Location: Department of Public Health Sciences (Clemson University's main campus, SC)
Term: renewable 12-month position
Clemson University's Center for Public Health Modeling and Response (PHMR) invites applications for a Postdoctoral Fellowship to join a CDC-funded center grant to create a national network in outbreak analytics and disease modeling. The mission of PHMR is to develop and utilize data-driven approaches to inform clinical and public health decision-making and assist the ability of health organizations and communities to prepare for and respond to, public health threats. The Postdoctoral Fellow will be a part of Clemson's DMA-PRIME division (Disease Modeling and Analytics to inform outbreak Preparedness, Response, Intervention, Mitigation, and Elimination). For this position, we seek an enthusiastic, collaborative researcher with training in data science with an interest in developing and implementing artificial intelligence (AI) and machine learning (ML) techniques to inform and improve outbreak detection, forecasting, and public health response nationwide.
Duties and responsibilities: The postdoctoral fellowship will focus on developing and implementing AI and ML methodology, with the ultimate goal of detecting/ predicting disease hotspots and informing real-time outbreak response efforts. The models and predictions generated will be integrated into health systems across South Carolina to 1) inform and improve strategic delivery of mobile health clinics for infectious disease testing, treatment, and vaccination, 2) inform community awareness, and 3) run statewide disaster simulation scenarios. The postdoc will be expected to lead publications for the models developed and collaborate on (or lead) publications in public health, medical, and policy journals for research related to the implementation of the methods developed.
Supervision
The Postdoctoral Fellow's main appointment will be within the Center for Public Health Modeling and Response, located within the Department of Public Health Sciences. Dr. Rennert will serve as the primary advisor, with Dr. McMahan, Dr. Wu, and Dr. Iuricich as co-advisors. The appointment is funded for 2 years, with future years contingent on funding/performance. Salary is highly competitive with a competitive benefits package and negotiable start date.
Qualifications
Required Qualifications
• A PhD in data science or related fields, with demonstrated knowledge in machine learning, deep learning, or statistical analysis.
• Proficiency in programming languages commonly used in data science and AI, with a strong emphasis on Python. Knowledge of other languages such as R, MATLAB, or C plus plus may be beneficial.
• Proficiency in data preprocessing, cleaning, and feature engineering. Familiarity with tools and libraries for data manipulation, such as pandas and NumPy.
• Expertise in machine learning frameworks and libraries, like TensorFlow, PyTorch, or scikit-learn.
Desirable Qualifications
• Specialized expertise in a particular subfield of AI, such as natural language processing, generative adversarial networks, or transformers.
• Domain-specific knowledge, such as healthcare, medicine
• A history of publishing research findings in conferences, journals, or other relevant platforms.
Application Instructions
Application Instructions: Applicants should submit a cover letter, CV, and contact information for three references through Interfolio: http://apply.interfolio.com/136202
Inquiries should be sent to Dr. Lior Rennert (liorr@clemson.edu) and Dr. Federico Iuricich (fiurici@clemson.edu).
Salary: $65,000
Benefits: https://www.clemson.edu/human-resources/benefits/index.html

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