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Data Science Cardiology Jobs (NOW HIRING)

Prolaio

Sr. Data Scientist, Clinical

Chicago, IL · On-site

This is precision cardiology, and we know it's within reach. What Will You Do? The Overview The Senior Data Scientist, Clinical will leverage advanced data science methodologies to advance the ...

Prolaio

Sr. Data Scientist, Clinical

Chicago, IL

This is precision cardiology, and we know it's within reach. What Will You Do? The Overview The Senior Data Scientist, Clinical will leverage advanced data science methodologies to advance the ...

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$122.7K

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How much do data science cardiology jobs pay per year?

As of Jun 7, 2026, the average yearly pay for data science cardiology 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 the difference between Data Science Cardiology vs Data Analyst Cardiology?

AspectData Science CardiologyData Analyst Cardiology
Required CredentialsAdvanced degrees (Master's/PhD), programming skills, statistical knowledgeBachelor's degree, basic data analysis skills
Work EnvironmentResearch settings, hospitals, healthcare tech companiesClinical departments, healthcare providers, administrative offices
Employer & Industry UsageMedical research institutions, biotech firms, hospitalsHospitals, clinics, healthcare organizations
Common Search & ComparisonMore technical, predictive modeling, machine learningData reporting, dashboards, descriptive analysis

Data Science Cardiology involves advanced analytics, machine learning, and predictive modeling, often requiring higher education and technical skills. Data Analyst Cardiology focuses on interpreting healthcare data, creating reports, and supporting clinical decisions. Both roles are vital in healthcare but differ in complexity and scope.

How does a Data Science professional in cardiology typically collaborate with clinicians and medical researchers?

Data Science professionals in cardiology work closely with clinicians and medical researchers to analyze complex health data, develop predictive models, and generate actionable insights for patient care. Regular collaboration often involves participating in interdisciplinary meetings, translating clinical questions into data-driven analyses, and communicating findings in a clear, accessible manner. Building strong relationships with healthcare teams is essential, as it ensures that the developed models and algorithms are practical and aligned with real-world clinical needs.

What is data science in cardiology?

Data science in cardiology is the application of advanced data analysis, machine learning, and statistical techniques to cardiovascular research and clinical care. It involves analyzing large datasets such as electronic health records, medical images, and patient monitoring data to uncover patterns, predict outcomes, and improve diagnosis and treatment of heart diseases. Data scientists in cardiology collaborate with clinicians and researchers to develop models that assist in personalized medicine, risk assessment, and early detection of cardiac conditions.

What are the key skills and qualifications needed to thrive as a Data Science professional in Cardiology, and why are they important?

To thrive as a Data Science professional in Cardiology, you need a strong background in statistics, machine learning, and domain knowledge in cardiovascular medicine, typically supported by a degree in data science, computer science, or biomedical engineering. Familiarity with tools like Python, R, SQL, and healthcare data systems such as electronic health records (EHRs), as well as relevant certifications, is highly valuable. Strong analytical thinking, collaboration with clinicians, and effective communication skills are essential soft skills for translating complex data into actionable clinical insights. These competencies are crucial for developing accurate predictive models and supporting evidence-based decision-making to improve patient outcomes in cardiology.
Infographic showing various Data Science Cardiology job openings in the United States as of May 2026, with employment types broken down into 2% As Needed, 73% Full Time, 24% Part Time, and 1% Contract. Highlights an 97% Physical, 1% Hybrid, and 2% Remote job distribution, with an average salary of $122,738 per year, or $59 per hour.

Sr. Data Scientist, Clinical

Prolaio

Chicago, IL • On-site

Full-time

Medical, Dental, Vision, Life, Retirement, PTO

Posted 13 days ago

Job description

Who Are We?
Prolaio believes that continuous learning and collaboration can make a significant difference in how heart care is administered. We are creating smarter ways to address heart disease and heart risks by uniting patients, care teams, and researchers on a secure, technology-enabled platform that drives clinical innovation and offers a path towards better patient outcomes.
This is precision cardiology, and we know it's within reach.
What Will You Do?
The Overview
The Senior Data Scientist, Clinical will leverage advanced data science methodologies to advance the science and clinical applications of digital biomarkers. This role involves developing rigorous technical plans and executing complex analyses on multimodal datasets (digital biomarkers from wearable data, electronic health records [EHR], claims) for publication in high-impact medical journals. The successful candidate will build pipelines to prepare analytic datasets from wearable data and EHR and utilize Python and/or R to develop multimodal risk prediction models to describe, predict, and estimate clinical effects.
The Specifics
  • Clinical Analysis & Publication: Design and execute statistical analyses on large clinical datasets. Author abstracts, statistical analysis plans, conference presentations, and manuscripts for publication in peer-reviewed medical journals.
  • Data Pipeline Development: Build, document, and maintain reproducible data pipelines to curate analytic datasets, combining data from multiple assets (e.g., continuous signal data, claims, electronic health records, etc.).
  • Risk Prediction Modeling: Develop and deploy time-varying and multimodal risk prediction models which extract insights from contextual health data and physiologic signals
  • Scientific Leadership: Contribute to rigorous science that expands our understanding of digital biomarkers and clinical endpoints in cardiovascular disease in order to enable Prolaio's ability to support clinical research and cardiovascular care.
  • Cross-Functional Collaboration: Collaborate cross-functionally with data engineering, operations, clinical, and other teams to ensure data analyses and modeling pipelines align with cross-team standards, scientific validity and company objectives.
  • Advanced Data Abstraction: Utilize both traditional programmatic and (where applicable) modern LLM-based techniques for complex data processing and clinical abstraction.

Why Prolaio?
  • Impactful Work: You will join in the fight against heart failure (HF) and hypertrophic cardiomyopathy (HCM) with the goal of extending and saving the lives of our patients while also being at the forefront of changing the healthcare industry through technology.
  • Innovative Environment: You will be part of an organization doing something that's never been done before.
  • Professional Growth: You will join a growing team and have a substantial impact on our daily and future operations with the opportunity to continuously learn and grow.
  • Collaborative Team: You will be part of a team of collaborative, curious, and committed individuals focused on the collective good, inclusiveness, scientific excellence, and advancing digital health for cardiology.

Who You Are?
  • Education & Experience: PhD, MD, or master's degree. 3+ years of academic or industry experience post-PhD/MD or 5+ years post-master's in any of the following fields: applied statistics, biostatistics, epidemiology, health economics, data science, health informatics, or a related field.
  • Scientific Track Record: A strong track record of peer-reviewed scientific publications, with experience communicating scientific results through presentations, abstracts, and manuscripts.
  • Healthcare Data Expertise: Experience preparing and analyzing large healthcare data sets, such as claims, electronic health records, or clinical trials. Experience with the specification of clinical event definitions and familiarity with healthcare data standards/ontologies (e.g., FHIR, OMOP, ICD-10, CPT).
  • Time-Series Data: Experience processing and analyzing high-volume time-series data.
  • Technical Proficiency: Experience in Python for machine learning and pipeline development. Experience in R for biostatistical inference is a plus.
  • Core Expertise: Deep expertise in at least TWO or more of the following three areas:
    • Agentic LLMs: Experience designing and validating LLM-based agentic pipelines (e.g., with LangChain, Vertex AI, etc.). Experience fine-tuning LLMs is a plus.
    • Machine learning for multimodal data: Completed projects in Python to develop predictive health risk models using common data sciences libraries (e.g., scikit-learn, etc.) and completed projects utilizing deep learning frameworks (e.g., PyTorch, Jax) for time-series, computer vision, or multimodal data.
    • Biostatistics & Epidemiology: Proven ability to implement models for statistical inference, with specific expertise in longitudinal health data, time-to-event (survival) analysis, and disease trajectories. Deep understanding of epidemiologic concepts (bias, confounding, data missingness) and familiarity with study design for observational studies and randomized controlled trials.
  • Tools for data science: Familiarity with modern coding standards for data science including reproducible environment management (e.g. poetry, uv, renv), version control (Git), robust documentation, report generation (e.g. Quarto), and SQL. Experience with production tools for continuous integration, deployment, and experiment tracking (e.g. MLflow and metaflow)
  • Communication: Ability to work cross-functionally and seamlessly translate highly technical concepts to non-technical audiences and stakeholders.

Additional Qualifications (Nice to Haves)
  • Prior research or industry experience in cardiovascular disease (CVD) or digital cardiology.
  • Prior experience with data from wearables or other sensor data.

Why You'll Love Working Here
  • Meaningful Compensation: Competitive salary, performance bonus, and equity so you can share in what we build.
  • Great Health Coverage: Medical, dental, and vision plans with multiple options and strong company contributions.
  • Flexible Spending Perks: HSA, FSA, commuter benefits, and a $1,200 annual Lifestyle Spending Account to support wellness, commuting, family needs, and more.
  • Time to Recharge: Generous paid time off, sick leave, and company holidays.
  • Family-First Benefits: Paid parental leave, caregiver leave, and support for growing families.
  • Security & Peace of Mind: Company-paid life insurance and short- and long-term disability coverage.
  • Plan for the Future: 401(k) plan to help you build long-term financial security.
  • Care When You Need It: Easy access to telehealth and optional supplemental coverage for life's unexpected moments.

Starting Salary is at $136,000.00 (Exact Compensation may vary based on skills, experience, and location)

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