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Health Data Science Fellow information

What is a health data science fellow?

A Health Data Science Fellow is a professional participating in a specialized fellowship program focused on applying data science techniques to healthcare data. Fellows work on projects involving the analysis of medical, clinical, or public health datasets to derive insights that can improve patient care, hospital operations, or health policy. These programs typically combine mentorship, hands-on experience with real-world datasets, and advanced training in statistics, programming, and machine learning. The role is ideal for individuals with a background in data science, statistics, or health sciences who want to deepen their expertise in health-related applications.

What types of projects can a health data science fellow expect to work on, and how do these contribute to team goals?

Health Data Science Fellows typically engage in projects such as analyzing large-scale healthcare datasets, developing predictive models for patient outcomes, or supporting clinical research with data-driven insights. These projects are often collaborative, involving close work with clinicians, IT professionals, and other data scientists to ensure analyses are relevant and actionable. Fellows contribute by turning complex data into meaningful recommendations that can improve patient care, operational efficiency, or public health initiatives. The role provides a unique opportunity to gain hands-on experience with real-world health data and make a measurable impact within multidisciplinary teams.

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

To thrive as a Health Data Science Fellow, you need strong analytical skills, proficiency in statistics, and a background in fields like public health, computer science, or bioinformatics, often supported by a relevant degree. Experience with programming languages (such as Python or R), data visualization tools, and familiarity with healthcare data systems are typically required. Excellent problem-solving, communication, and teamwork skills help fellows translate complex data insights into actionable healthcare solutions. These capabilities are crucial for driving data-driven improvements in health outcomes and supporting evidence-based decision-making in medical environments.

What is the difference between Health Data Science Fellow vs Data Analyst?

AspectHealth Data Science FellowData Analyst
Required CredentialsTypically advanced degrees in health informatics, data science, or related fields; some fellowships may require certificationsBachelor's or master's in data analysis, statistics, or related fields; certifications like CAP or Microsoft Certified Data Analyst are common
Work EnvironmentResearch institutions, healthcare organizations, or academic settings focusing on health data projectsBusiness, healthcare, or tech companies analyzing data to inform decisions
Employer & Industry UsagePrimarily in healthcare, research, and academic sectorsAcross various industries including healthcare, finance, marketing, and technology

In summary, a Health Data Science Fellow focuses on advanced health-related data projects often within research or academic settings, requiring specialized health informatics knowledge. A Data Analyst has a broader role across industries, analyzing data to support business decisions, often with more general data analysis skills.

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Health Data Science Fellow jobs near you
Infographic showing various Health Data Science Fellow job openings in the United States as of August 2026, with employment types broken down into 2% As Needed, 76% Full Time, 16% Part Time, and 6% Contract. Highlights an 95% Physical, 1% Hybrid, and 4% Remote job distribution.

Post Doctoral Fellow-MSH-76880-013

Mount Sinai Health System

Manhattan, NY • On-site

$54K - $73K/yr

Full-time

Re-posted 8 days ago

Mount Sinai rating

7.7

Company rating: 7.7 out of 10

Based on 296 frontline employees who took The Breakroom Quiz

160th of 891 rated healthcare providers

Job description

Job Summary:
Mount Sinai Health System is a globally recognized leader in medical education, scientific research, and innovative patient care. The Suarez-Farinas Lab is seeking a highly motivated postdoctoral fellow to work at the intersection of artificial intelligence, causal inference, and translational health data science, focusing on developing statistical and machine learning methods to advance precision medicine.
Responsibilities:
• designing studies
• developing novel algorithms
• analyzing large and complex datasets
• collaborating closely with clinicians and interdisciplinary teams
• contributing to high-impact publications
• presenting at leading conferences
• mentoring junior trainees
Qualifications:
Required:
• PhD in statistics, biostatistics, computer science, data science, bioinformatics, or a related quantitative field
• Strong background in ML and interest or experience in causal inference (e.g., causal ML, treatment effect estimation)
• Proficiency in R and/or Python, with experience handling large, complex datasets
• Solid understanding of statistical modeling, experimental design, and algorithm development
• Demonstrated ability to work independently and collaboratively in a multidisciplinary environment
• Strong written and oral communication skills
Preferred:
• Experience in biomedical or healthcare research is a plus
• Experience mentoring or teaching is desirable
Company:
Mount Sinai Health System delivers integrated medical care, research, and medical education through its network. Founded in 2013, the company is headquartered in New York, USA, with a team of 10001+ employees. The company is currently Late Stage.

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