Sr. Data Scientist, Clinical

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 Lang Chain, 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,…