Sr. Data Scientist, Clinical

Prolaio • Chicago, IL

3h

About The Position

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.

Requirements

• 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.
• A strong track record of peer-reviewed scientific publications, with experience communicating scientific results through presentations, abstracts, and manuscripts.
• 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).
• Experience processing and analyzing high-volume time-series data.
• Experience in Python for machine learning and pipeline development.
• Deep expertise in at least TWO or more of the following three areas: Agentic LLMs, Machine learning for multimodal data, Biostatistics & Epidemiology.
• 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.
• Ability to work cross-functionally and seamlessly translate highly technical concepts to non-technical audiences and stakeholders.

Nice To Haves

• Experience in R for biostatistical inference is a plus.
• Experience designing and validating LLM-based agentic pipelines (e.g., with LangChain, Vertex AI, etc.). Experience fine-tuning LLMs is a plus.
• 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.
• 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.
• Experience with production tools for continuous integration, deployment, and experiment tracking (e.g. MLflow and metaflow).
• Prior research or industry experience in cardiovascular disease (CVD) or digital cardiology.
• Prior experience with data from wearables or other sensor data.

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 in order 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

• Competitive salary
• performance bonus
• equity
• Medical, dental, and vision plans with multiple options and strong company contributions.
• HSA, FSA, commuter benefits, and a $1,200 annual Lifestyle Spending Account
• Generous paid time off, sick leave, and company holidays.
• Paid parental leave, caregiver leave, and support for growing families.
• Company-paid life insurance and short- and long-term disability coverage.
• 401(k) plan
• Easy access to telehealth and optional supplemental coverage

What This Job Offers

• Job Type: Full-time
• Career Level: Senior