Full-Time

Senior Data Scientist

Clinical

Prolaio

Prolaio

51-200 employees

Real-time cardiovascular care analytics platform

Compensation Overview

$136k/yr

+ Bonus + Equity

Chicago, IL, USA

In Person

Category
Data & Analytics (1)
Required Skills
LLM
Python
R
Git
SQL
MLflow
biostatistics
Requirements
  • 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
  • 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; experience in R for biostatistical inference is a plus
  • Deep expertise in at least two of the following: Agentic large language models (LLMs); Machine learning for multimodal data; Biostatistics and epidemiology
  • Familiarity with modern coding standards for data science including reproducible environment management (for example 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)
  • Ability to work cross-functionally and translate highly technical concepts to non-technical audiences and stakeholders
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)
  • 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 understanding of digital biomarkers and clinical endpoints in cardiovascular disease to enable 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 modern LLM-based techniques for complex data processing and clinical abstraction
Desired Qualifications
  • Prior research or industry experience in cardiovascular disease or digital cardiology
  • Prior experience with data from wearables or other sensor data

Prolaio provides a cardiovascular-focused health tech platform that helps healthcare providers improve patient outcomes through real-time data analytics and continuous monitoring. It collects data from electronic health records, patient surveys, and wearable sensors to build predictive models that warn care teams about potential health issues before they become critical. Healthcare providers access the platform via a subscription, giving them real-time analytics, alerts, and evidence-based care planning tools to monitor patients and optimize treatments. The company differentiates itself by concentrating on cardiovascular care and enabling continuous learning, patient engagement, and collaboration with pharmaceutical companies and research institutions to support new therapies. The overall goal is to reduce hospital admissions and readmissions while delivering timely, data-driven care that keeps patients healthier.

Company Size

51-200

Company Stage

Early VC

Total Funding

$25.5M

Headquarters

Scottsdale, Arizona

Founded

2022

Your Connections

People at Prolaio who can refer or advise you

Simplify Jobs

Simplify's Take

What believers are saying

  • Kardigan acquisition integrates Prolaio's data platform with cardiac intelligence.
  • Supports all-remote heart studies generating high-density CV insights.
  • Curates largest CV dataset to prevent avoidable heart events.

What critics are saying

  • Data incompatibility with Kardigan's R&D systems erodes value in 12-18 months.
  • FDA scrutiny halts platform use in trials and triggers fines in 6-12 months.
  • Key personnel loss stalls analytics updates in 3-6 months.

What makes Prolaio unique

  • Prolaio's FDA-cleared software integrates real-world CV data with proprietary algorithms.
  • Comprehensive patient kit includes wrist monitor, smart cuff, scale, and ECG patches.
  • Tech-enabled trial enrichment optimizes CV study design and boosts statistical power.

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Benefits

Health Insurance

Dental Insurance

Vision Insurance

Life Insurance

Disability Insurance

Health Savings Account/Flexible Spending Account

Unlimited Paid Time Off

Flexible Work Hours

Remote Work Options

Paid Vacation

Paid Sick Leave

Paid Holidays

Hybrid Work Options

401(k) Retirement Plan

401(k) Company Match

Performance Bonus

Stock Options

Company Equity

Retirement Plan: 401(k) plan

Paid Time Off

Growth & Insights and Company News

Headcount

6 month growth

-1%

1 year growth

7%

2 year growth

0%
Business Wire
Mar 28th, 2025
Kardigan Acquires Prolaio to Create a Pioneering Heart Health Company with One-of-a-Kind Integrated Cardiovascular Data and Therapeutics Platform

Kardigan, a heart health company modernizing cardiovascular (CV) drug development, announced today that it has acquired Prolaio, a clinical intelligence comp...

Securities and Exchange Commission
Sep 13th, 2023
SEC FORM D

The Securities and Exchange Commission has not necessarily reviewed the information in this filing and has not determined if it is accurate and complete.The reader should not assume that the information is accurate and complete.