What is Ansible Health?
Ansible Health unites physicians, nurses, respiratory therapists, and tech experts from premier institutions like Columbia, Johns Hopkins, Google, and Amazon to revolutionize care for patients with polychronic conditions, particularly those with a cardiopulmonary focus. We merge elite clinical expertise with cutting-edge technology to deliver personalized, efficient care at scale. Our team's groundbreaking research, published in leading journals like Nature and PLOS, underscores our commitment to innovation. Backed by Bessemer, First Round Capital, AlleyCorp, Breyer Capital, and Inflect Capital, we're a well-funded Series A startup partnering with leading health systems, payors, and providers nationwide. Our mission is genuinely improving patient outcomes through data-driven care, not just optimizing metrics. If you're passionate about transforming healthcare through technology and data, join our diverse team in making a tangible impact.
The Job at a Glance
We are seeking an exceptional Data Scientist to join our innovative team. In this role, you will develop and deploy sophisticated machine learning models and advanced analytics solutions to transform patient care for complex cardiopulmonary conditions. You'll leverage clinical, operational, and device-generated data to build predictive algorithms that support clinical decision-making, optimize care pathways, and improve patient outcomes.
Working at the intersection of healthcare and cutting-edge data science, you'll collaborate with our interdisciplinary team of clinicians, engineers, and researchers to translate scientific insights into practical applications. This position offers a unique opportunity to apply advanced statistical and machine learning techniques to solve meaningful challenges in healthcare while contributing to our peer-reviewed research initiatives.
Key Responsibilities
Design, develop, and implement machine learning models and algorithms to predict clinical outcomes, identify at-risk patients, and optimize treatment protocols for polychronic cardiopulmonary conditions
Apply advanced statistical methods to analyze complex patient data, extracting meaningful patterns and insights that drive clinical and operational improvements
Build and deploy natural language processing solutions to extract valuable information from unstructured clinical notes and medical literature
Develop time-series models to analyze continuous monitoring data from medical devices and wearables, identifying early warning signs of patient deterioration
Create and validate clinical decision support algorithms that provide actionable insights for healthcare providers
Qualifications
Master's degree or PhD in Data Science, Machine Learning, Computer Science, Statistics, Biostatistics, or a related quantitative field
Proven experience applying machine learning and statistical modeling to solve real-world problems, preferably in healthcare or life sciences
Strong programming skills in Python, including proficiency with scientific computing libraries (NumPy, SciPy, pandas) and ML frameworks (PyTorch, TensorFlow, scikit-learn)
Experience building and deploying machine learning models in production environments
Advanced knowledge of statistical analysis, experimental design, and causal inference
Preferred Skills
Experience with healthcare data, particularly EHR systems, medical imaging, or clinical time-series data
Knowledge of FHIR standards and healthcare interoperability frameworks
Familiarity with cardiopulmonary conditions, respiratory care, or chronic disease management
What We Offer
Opportunity to make a significant impact on patient care for complex conditions and shape the future of digital health.
A highly collaborative, mission-driven, and innovative work environment.
Exposure to cutting-edge health technology, AI applications, and data practices.
Competitive salary, equity options, and comprehensive benefits package.
Significant opportunities for professional development and career growth.
Work alongside experts from top clinical and technology institutions.