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Healthcare Data Science Jobs in Silver Spring, MD

EXL

Technical Project Manager - Data Science

Washington, DC

$58.50 - $79.25/hr

Data Science-Machine Learning Engineer As a Project Manager, you will lead a strategic healthcare initiative with a strong focus on STARS performance and outcomes. You will drive a cross-functional ...

Apex Systems

Healthcare Data Analyst

Hyattsville, MD

$65/hr

Role Overview The Healthcare Data Analyst serves as a subject matter expert supporting financial and claims-based analytics. This role focuses on claims analysis, forecasting, trend evaluation, and ...

ADVI Health

Senior Data Scientist-RWE

Washington, DC · On-site

$126K - $158K/yr

In this role, you'll leverage advanced data science and health economics methods to help shape access to life-saving therapies and innovations. You'll work with rich, real-world healthcare data ...

BullFrogAI

Principal Data Scientist

Gaithersburg, MD · On-site

$175K - $215K/yr

Design, implement, and optimize AI/ML models using diverse healthcare data types, including omics ... science, engineering, or related Experience • 5+ years of post-graduate experience in life ...

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All JobsHealthcare Data Science JobsHealthcare Data Science Jobs in Silver Spring, MD

Healthcare Data Science information

See Silver Spring, MD salary details

$47.6K

$170.6K

$251.7K

How much do healthcare data science jobs pay per year?

As of Jun 12, 2026, the average yearly pay for healthcare data science in Silver Spring, MD is $170,592.00, according to ZipRecruiter salary data. Most workers in this role earn between $138,000.00 and $175,700.00 per year, depending on experience, location, and employer.

Can data scientists make $300k?

Healthcare data scientists can potentially earn $300,000 or more annually, especially with extensive experience, advanced skills in machine learning and statistical analysis, and work in high-paying industries or senior roles. Achieving this level often requires a strong educational background, certifications, and a track record of impactful projects.

What are the key skills and qualifications needed to thrive as a Healthcare Data Scientist, and why are they important?

To thrive as a Healthcare Data Scientist, you need a strong background in statistics, data analysis, programming (Python or R), and an understanding of healthcare systems, often supported by a degree in data science, computer science, or a related field. Experience with tools like SQL, machine learning frameworks, and familiarity with electronic health record (EHR) systems are typically required. Strong problem-solving skills, attention to detail, and effective communication help translate complex data insights into actionable healthcare solutions. These skills are crucial for deriving meaningful insights from complex healthcare data, improving patient outcomes, and supporting evidence-based decision-making.

What is the difference between Healthcare Data Science vs Healthcare Data Analysis?

AspectHealthcare Data ScienceHealthcare Data Analysis
Required CredentialsTypically requires a degree in data science, statistics, or related fields; often includes programming skills and knowledge of machine learningUsually requires a background in healthcare, statistics, or data analysis; may include certifications in data analysis tools
Work EnvironmentInvolves developing models, algorithms, and predictive analytics; often in research or tech-focused settingsFocuses on interpreting data, generating reports, and supporting clinical or administrative decisions
Employer & Industry UsageUsed by healthcare tech companies, research institutions, and large healthcare providersCommon in hospitals, clinics, insurance companies, and healthcare consulting firms

Healthcare Data Science and Healthcare Data Analysis share overlapping skills but differ mainly in scope. Data scientists develop advanced models and predictive tools, while data analysts focus on interpreting data and generating insights. Both roles are vital in healthcare but serve different functions within the industry.

Is 40 too late for data science?

Healthcare Data Science is a field that values skills and experience over age; many professionals transition into data science later in their careers. Gaining proficiency in programming, statistics, and tools like Python or R can enable a successful shift regardless of age. Continuous learning and relevant certifications can enhance employability at any age.

What are some common challenges faced by healthcare data scientists when working with clinical data?

Healthcare data scientists often navigate challenges such as dealing with incomplete or inconsistent medical records, ensuring patient privacy and data security, and integrating data from diverse sources like electronic health records, lab results, and imaging systems. Additionally, they must collaborate closely with clinicians and IT staff to interpret complex datasets accurately and ensure that their analyses have practical clinical value. Maintaining compliance with healthcare regulations, such as HIPAA, is also a critical aspect of the role.

What do data scientists in healthcare do?

Healthcare data scientists analyze medical data to identify patterns, improve patient outcomes, and support clinical decision-making. They use statistical tools, programming languages like Python or R, and machine learning techniques to interpret large datasets from electronic health records, imaging, and research studies.

What can you do with a degree in health data science?

A degree in health data science prepares individuals for roles such as healthcare data analyst, data scientist, or informaticist, where they analyze medical data, develop predictive models, and support clinical decision-making. Skills in programming, statistics, and knowledge of healthcare systems are essential for these positions.

What is healthcare data science?

Healthcare data science is a field that uses data analysis, statistics, and machine learning to extract insights from health-related data. Professionals in this area work with large datasets from sources like electronic health records, clinical trials, and wearable devices. Their goal is to improve patient outcomes, optimize hospital operations, and support medical research by turning raw data into actionable information. This field requires knowledge of healthcare systems, data management, and advanced analytics techniques.
What are the most commonly searched types of Healthcare Data Science jobs in Silver Spring, MD? The most popular types of Healthcare Data Science jobs in Silver Spring, MD are:
What are popular job titles related to Healthcare Data Science jobs in Silver Spring, MD? For Healthcare Data Science jobs in Silver Spring, MD, the most frequently searched job titles are:
What job categories do people searching Healthcare Data Science jobs in Silver Spring, MD look for? The top searched job categories for Healthcare Data Science jobs in Silver Spring, MD are:
What cities near Silver Spring, MD are hiring for Healthcare Data Science jobs? Cities near Silver Spring, MD with the most Healthcare Data Science job openings:
Healthcare Data Science jobs near you
Infographic showing various Healthcare Data Science job openings in Silver Spring, MD as of June 2026, with employment types broken down into 3% As Needed, 83% Full Time, 11% Part Time, and 3% Contract. Highlights an 89% Physical, 3% Hybrid, and 8% Remote job distribution, with an average salary of $170,592 per year, or $82 per hour.
Technical Project Manager - Data Science

Technical Project Manager - Data Science

EXL

Washington, DC

$58.50 - $79.25/hr

Other

Posted 4 days ago

ExlService Holdings rating

8.3

Company rating: 8.3 out of 10

Based on 7 frontline employees who took The Breakroom Quiz

58th of 426 rated business services

Job description

Data Science-Machine Learning Engineer

As a Project Manager, you will lead a strategic healthcare initiative with a strong focus on STARS performance and outcomes. You will drive a cross-functional program involving Machine Learning, Data Engineering, and data intelligence, ensuring seamless collaboration between business stakeholders and technical teams.

In this role, you will be responsible for translating business objectives into actionable delivery plans, aligning data, analytics, and reporting workstreams to support quality improvement and operational efficiency. You will work closely with stakeholders, product teams, and engineering teams to identify opportunities, prioritize initiatives, and deliver scalable data-driven solutions that enhance performance and decision-making aligned with STARS goals.

Essential Skills and Qualifications:
  1. 5+ Years of experience in AI/ML, Data Science, or Data Engineering roles with team leadership
  2. Strong understanding of Machine Learning and Data Science concepts, including model development, feature engineering, and evaluation
  3. Proficiency in Python and PySpark for large-scale data processing and model development
  4. Experience in building and managing end-to-end data pipelines (ETL/ELT)
  5. Experience with MLOps practices, including model deployment, monitoring, and lifecycle management
  6. Strong understanding of data quality, governance, and scalable data architecture
  7. Experience leading cross-functional teams including ML engineers, data engineers, and BI developers
  8. Exposure to visualization tools like Power BI and ability to connect insights to business KPIs
  9. Strong understanding of healthcare data and STARS metrics (preferred)
  10. Excellent communication and stakeholder management skills
  11. Strong analytical, problem-solving, and decision-making abilities
  12. Ability to translate complex business problems into data-driven AI/ML solutions
Qualifications:
  1. 5+ Years of experience in AI/ML, Data Science, or Data Engineering roles with team leadership
  2. Strong understanding of Machine Learning and Data Science concepts, including model development, feature engineering, and evaluation
  3. Proficiency in Python and PySpark for large-scale data processing and model development
  4. Experience in building and managing end-to-end data pipelines (ETL/ELT)
  5. Experience with MLOps practices, including model deployment, monitoring, and lifecycle management
  6. Strong understanding of data quality, governance, and scalable data architecture
  7. Experience leading cross-functional teams including ML engineers, data engineers, and BI developers
  8. Exposure to visualization tools like Power BI and ability to connect insights to business KPIs
  9. Strong understanding of healthcare data and STARS metrics (preferred)
  10. Excellent communication and stakeholder management skills
  11. Strong analytical, problem-solving, and decision-making abilities
  12. Ability to translate complex business problems into data-driven AI/ML solutions

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