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Health Data Science Fellow Jobs (NOW HIRING)

Hispanic Health Council Inc

Data Engineer Fellow

Hartford, CT · On-site

$115K - $138K/yr

The National Hispanic Health Research Institute (NHHRI) is guided by the expertise and vision of ... Bachelor's degree (completed or in progress) in Computer Science, Data Science, Information Systems ...

Baltimore Orioles

Data Scientist Fellow

Baltimore, MD · On-site

$20/hr

Data Scientist Fellow Reports To: Assistant GM, Analytics Department: Baseball Analytics Job Type ... Science, or equivalent. * Experience with SQL. * Experience with R or Python and pragmatic ...

Central Health

Principal Data Scientist

Austin, TX · Hybrid

Serve as the organization's foremost technical expert in applied data science, statistical modeling, and machine learning as they relate to healthcare and population health. Establish and maintain ...

New

Central Health

Principal Data Scientist

Austin, TX · Hybrid

Serve as the organization's foremost technical expert in applied data science, statistical modeling, and machine learning as they relate to healthcare and population health. Establish and maintain ...

New

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Health Data Science Fellow information

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.

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 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 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.
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What job categories do people searching Health Data Science Fellow jobs look for? The top searched job categories for Health Data Science Fellow jobs are:
Health Data Science Fellow jobs near you
Infographic showing various Health Data Science Fellow job openings in the United States as of July 2026, with employment types broken down into 1% As Needed, 84% Full Time, 12% Part Time, and 3% Contract. Highlights an 88% Physical, 2% Hybrid, and 10% Remote job distribution.
Data Engineer Fellow

Data Engineer Fellow

Hispanic Health Council Inc

Hartford, CT • On-site

$115K - $138K/yr

Full-time

Re-posted 6 days ago

Job description

About Company:

The National Hispanic Health Research Institute (NHHRI) is guided by the expertise and vision of some of the nation’s most accomplished and influential leaders. Our National Advisory Board brings together distinguished professionals from medicine, research, policy, technology, and community health to ensure our work is innovative, inclusive, and impactful. Together with our dedicated Board of Directors, they provide strategic direction and oversight to advance equitable health research for all communities.!

About the Role:

As a Data Engineer Fellow, you will support the development and maintenance of NHHRI’s cloud-based population health data warehouse by designing scalable schemas, integrating public-sector datasets, and ensuring data quality and reliability.

NHHRI seeks a Data Engineer Fellow to design and implement relational structures within the organization’s BigQuery environment. The Fellow will standardize, clean, and integrate federal and state datasets into a governed warehouse framework, supporting board reporting, research analysis, and future AI-enabled analytics.

This role focuses on building the structured data foundation that enables visualization, research, and strategic decision-making.

Minimum Qualifications:

    • Bachelor’s degree (completed or in progress) in Computer Science, Data Science, Information Systems, Statistics, or related field
    • Demonstrated proficiency in SQL, including joins, aggregations, and table creation (DDL)
    • Experience working with structured datasets (CSV, Excel, relational tables)
    • Experience cleaning, transforming, and standardizing data
    • Basic proficiency in Python (or similar scripting language) for data processing
    • Familiarity with geographic identifiers (e.g., FIPS, GEOID) or demonstrated ability to work with geographic data structures
    • Strong analytical and problem-solving skills
    • Ability to document data transformations and assumptions clearly
    • Ability to work independently and meet defined project milestones

Preferred Qualifications:

    • Experience working with cloud data warehouses e.g., BigQuery.
    • Experience designing relational schemas or dimensional models (e.g., fact and dimension tables)
    • Experience integrating public-sector datasets (e.g., Census, CDC, CMS, BLS)
    • Experience building or supporting ETL/ELT workflows
    • Familiarity with data governance concepts (metadata, lineage, documentation practices)
    • Exposure to AI/ML concepts or interest in applying AI techniques to structured population health datasets
    • Experience using AI-assisted data tools for cleaning or query generation
    • Interest in contributing to AI-enabled analytics built on top of structured data foundations

Responsibilities:


    • Design and implement scalable relational schemas in BigQuery
    • Develop and maintain core reference tables (e.g., dim_geography, dim_time)
    • Build and maintain standardized fact tables for integrated indicators
    • Load, clean, and standardize federal and state datasets
    • Harmonize mixed geographic levels (state, county, tract, ZIP as applicable)
    • Transform datasets into consistent long-format structures where appropriate
    • Write validation and quality-control SQL queries
    • Optimize tables using partitioning and clustering strategies
    • Monitor query efficiency and usage
    • Maintain organized raw, staging, and curated data structures
    • Document schema design decisions and transformation logic
    • Support reproducibility and governance standards
    • Collaborate with Visualization and Research Fellows to ensure consistent metric definitions.
  • NOTE: It's an unpaid fellowship.

Skills:

The ideal candidate demonstrates strong SQL proficiency and practical experience designing scalable relational schemas within a cloud-based data warehouse environment. They possess the ability to clean, standardize, and integrate structured public datasets across mixed geographies and time dimensions. The Fellow is detail-oriented, execution-driven, and capable of building reliable, well-documented data structures that support visualization, research analysis, and future AI-enabled analytics aligned with NHHRI’s long-term objectives.