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Remote Healthcare Data Science Jobs (NOW HIRING)

Senior Data Scientist

OR · On-site +1

$140K - $190K/yr

Minimum of 5+ years of hands-on data science or analytics experience, preferably in a healthcare, ... Remote

About the Role The Data Analyst plays a key role in monitoring, managing, and improving healthcare data file exchanges across clients, vendors, and internal systems. This role sits within a DataOps ...

About the Role The Data Analyst plays a key role in monitoring, managing, and improving healthcare data file exchanges across clients, vendors, and internal systems. This role sits within a DataOps ...

Senior Healthcare Data Engineer

$105K - $143K/yr

You can be in-office in either Portland, ME or Manchester, NH or potentially remote You Will ... A bachelor's degree in computer science, mathematics, statistics, economics, engineering, or ...

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Remote Healthcare Data Science information

What is the difference between Remote Healthcare Data Science vs Remote Healthcare Data Analyst?

AspectRemote Healthcare Data ScienceRemote Healthcare Data Analyst
Required CredentialsDegree in Data Science, Statistics, or related field; programming skills; knowledge of healthcare dataDegree in Data Analysis, Statistics, or related; proficiency in data tools; understanding of healthcare data
Work EnvironmentFocus on developing models, algorithms, and predictive analytics in healthcareFocus on interpreting data, generating reports, and supporting decision-making
Employer & Industry UsageUsed by healthcare tech companies, hospitals, research institutionsUsed by healthcare providers, insurance companies, consulting firms

Remote Healthcare Data Science involves building advanced models and algorithms to analyze healthcare data, requiring strong programming and statistical skills. In contrast, Remote Healthcare Data Analysts focus on interpreting data, creating reports, and supporting healthcare decision-making. Both roles are vital in healthcare analytics but differ in technical complexity and responsibilities.

What are some common challenges faced by remote healthcare data scientists, and how can they be addressed?

Remote healthcare data scientists often encounter challenges related to data privacy regulations, fragmented data sources, and effective collaboration with clinical teams. Navigating strict patient confidentiality laws such as HIPAA requires meticulous handling of sensitive information, often necessitating secure remote access solutions. Additionally, integrating data from various electronic health record systems can be complex. To address these challenges, it's important to proactively communicate with IT and compliance teams, use secure data transfer tools, and participate in regular virtual meetings with cross-functional healthcare professionals to ensure alignment and data integrity.

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

To thrive as a Remote Healthcare Data Scientist, you need a strong background in statistics, machine learning, and healthcare analytics, typically supported by a degree in data science, statistics, computer science, or a related field. Familiarity with programming languages like Python or R, data visualization tools, and experience with healthcare data systems such as EHRs or HIPAA-compliant databases are crucial. Strong problem-solving abilities, effective communication, and the capability to work independently are essential soft skills for this remote role. These skills enable professionals to extract actionable insights from complex healthcare data, ensuring data-driven decision-making and improved patient outcomes.

Can I get a remote job as a data scientist?

Remote healthcare data science jobs are available and often require strong skills in programming, statistical analysis, and familiarity with healthcare data. Many companies offer remote positions that involve data analysis, machine learning, and reporting, with some requiring certifications or experience in healthcare or related fields.

What is a Remote Healthcare Data Scientist?

A Remote Healthcare Data Scientist is a professional who analyzes health-related data to extract insights and improve patient outcomes, all while working from a location outside of traditional healthcare settings such as hospitals or clinics. They use statistical analysis, machine learning, and data visualization to interpret large datasets from electronic health records, clinical trials, and other health sources. Remote Healthcare Data Scientists collaborate with healthcare professionals and IT teams to develop predictive models, support research, and enhance decision-making processes, often using secure telecommunication and cloud-based tools. Their work can inform policy, improve care delivery, and support public health initiatives.

Is 40 too late for data science?

Age is not a barrier to entering remote healthcare data science roles, as skills in programming, statistics, and healthcare knowledge are more important. Many professionals successfully transition into data science later in their careers by gaining relevant certifications and building a portfolio. Continuous learning and adapting to new tools like Python, R, and machine learning frameworks are key to success regardless of age.

How to become remote healthcare data analyst?

To become a remote healthcare data analyst, you typically need a bachelor's degree in data science, statistics, or a related field, along with strong skills in programming languages like Python or R and experience with healthcare data systems. Gaining certifications such as Certified Health Data Analyst (CHDA) can enhance your qualifications, and proficiency with tools like SQL and electronic health records is often required. Remote roles also demand good communication skills and self-discipline to manage tasks independently.

Can a data scientist work in healthcare?

A data scientist can work in healthcare by analyzing medical data, developing predictive models, and supporting clinical decision-making. Healthcare data science often requires knowledge of healthcare systems, statistical skills, and tools like Python or R, along with understanding privacy regulations such as HIPAA.
More about Remote Healthcare Data Science jobs
What cities are hiring for Remote Healthcare Data Science jobs? Cities with the most Remote Healthcare Data Science job openings:
What are the most commonly searched types of Healthcare Data Science jobs? The most popular types of Healthcare Data Science jobs are:
What states have the most Remote Healthcare Data Science jobs? States with the most job openings for Remote Healthcare Data Science jobs include:
Infographic showing various Remote Healthcare Data Science job openings in the United States as of June 2026, with employment types broken down into 62% Full Time, 15% Part Time, and 23% Contract. Highlights an 100% Remote job distribution.

Senior Data Scientist

OneStudyTeam

OR • On-site, Remote

$140K - $190K/yr

Other

Posted 6 days ago


Job description

As a Senior Data Scientist, you will play a pivotal role in advancing Reify Health's data-driven solutions for clinical trials. In this position, you will drive the development of statistical models and machine learning algorithms to improve patient enrollment and trial management. You'll work in a highly regulated healthcare data environment, ensuring compliance with privacy standards while innovating on predictive analytics. This role involves close collaboration with cross-functional teams (especially ML Engineering) to translate complex data insights into practical, impactful tools for the clinical research community.

What You'll Be Working On
  • Site Randomization Forecasting: Develop/enhance forecasting models for site randomization and enrollment trends, enabling better planning and resource allocation across trial sites. 
  • Patient Matching/Ranking Algorithms: Support projects to build algorithms that intelligently match patients to (or rank patients for) appropriate clinical trials, enhancing recruitment efficiency and patient inclusion. 
  • Develop Other Advanced Statistical Models: Create and refine predictive models (Bayesian inference, regression analysis, time-series forecasting) to address other key clinical trial challenges and improve decision-making. 
  • AI Monitoring and Bias Detection: Implement processes to monitor machine learning models in production, detecting bias or performance drift and ensuring models remain fair, accurate, and compliant. 
  • Data Pipeline & Tooling Development: Build and optimize data pipelines and analytical workflows using tools like AWS Athena, Redshift, SageMaker, and dbt, enabling scalable model training and deployment. 
  • Regulatory Compliance in Data Science: Ensure all data science practices align with HIPAA, GDPR, and other privacy regulations, integrating compliance considerations into model development and data handling. 
  • Cross-Functional Collaboration: Work closely with machine learning engineers, product managers, and other stakeholders to integrate models into products and clearly communicate insights and recommendations. 
What You Bring to OneStudyTeam
  • Education: Minimum of Master's or Ph.D. in Statistics, Data Science, Computer Science, or a related quantitative field (or equivalent professional experience). 
  • Experience: Minimum of 5+ years of hands-on data science or analytics experience, preferably in a healthcare, clinical research, or other highly regulated data environment.
  • Statistical & ML Expertise: Strong foundation in statistical modeling and machine learning techniques, including experience with Bayesian methods, regression analysis, and time-series forecasting. 
  • Model Monitoring & Fairness: Proficiency in evaluating model performance and bias, with the ability to implement AI monitoring tools and bias mitigation strategies to ensure ethical and reliable outcomes. 
  • Technical Toolset: Advanced programming skills in Python (with libraries such as scikit-learn, PyMC, mlforecast, etc.) and SQL, as well as familiarity with data transformation tools like dbt. 
  • Cloud & Data Infrastructure: Hands-on experience with cloud-based analytics and ML services, especially AWS tools (Athena for querying, Redshift for data warehousing, SageMaker for model development/deployment). 
  • Regulated Data Handling: Experience working with sensitive healthcare or clinical trial data under regulations like HIPAA and GDPR, demonstrating a deep commitment to data privacy and security best practices. 
  • Collaborative Communication: Excellent teamwork and meticulous verbal/written communication abilities, with a track record of partnering with engineering and product teams to translate data science work into actionable business solutions. 
  • Domain Knowledge: Understanding of clinical research or health-tech environments is highly valuable, including insight into clinical trial operations and a passion for improving patient outcomes through data.

The expected pay range for this role is $140,000 - $190,000 USD per year for full time team members.

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