Sr. Data Scientist, Clinical

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? 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. Responsibilities 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. 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.). Develop and deploy time-varying and multimodal risk prediction models which extract insights from contextual health data and physiologic signals. Contribute to rigorous science that expands our understanding of digital biomarkers and clinical endpoints in cardiovascular disease to enable Prolaio’s ability to support clinical research and cardiovascular care. 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. Utilize both traditional programmatic and (where applicable) modern LLM-based techniques for complex data processing and clinical abstraction. Benefits Impactful Work: You will join 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. Qualifications 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 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 science 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 (poetry, uv, renv), version control (Git), robust documentation, report generation (Quarto), and SQL. Experience with production tools for continuous integration, deployment, and experiment tracking (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. Compensation Starting Salary is at $136,000.00. Exact Compensation may vary based on skills, experience, and location. EEO Statement We invite applicants to share their demographic background. We do not discriminate on the basis of any protected group status under any applicable law. We are an equal opportunity employer. #J-18808-Ljbffr Prolaio