Junior Data Analyst (W2, Remote)

caretalkhealth; United States; Remote; USD 40,000 - 57,000; Full time

Job summary

caretalkhealth is seeking an entry-level data analyst for a fully remote, US-based position. You will manage, explore, and interpret healthcare datasets that support a virtual medical practice, spanning data engineering, statistics, and applied machine learning.

You will collaborate with product, engineering, and clinical stakeholders to surface insights that improve workflows and patient outcomes, build dashboards, validate data quality, and develop basic predictive models with guidance from

Qualifications

• Bachelor's degree in a quantitative field (Data Science, Statistics, CS, or related).
• 1–2 years in a data-focused role (internships or project work).
• Proficiency with SQL and at least one of Python or R for analysis.
• Familiarity with Tableau or Power BI for visualization.
• Working knowledge of statistical methods and introductory predictive modeling concepts.
• Clear written and verbal communication, attention to detail, and a strong learning mindset.

Responsibilities

• Collect, clean, and preprocess healthcare data from multiple sources.
• Run statistical analyses and translate findings into actionable recommendations.
• Build and maintain dashboards, reports, and visualizations for non-technical audiences.
• Collaborate with clinical, product, and engineering teams to scope data requirements.
• Validate and safeguard data accuracy, completeness, and integrity.
• Develop and test entry-level machine learning models to surface patterns.

Skills

SQL

Python

R

Statistical methods

Communication

Data cleaning

Education

Bachelor's degree in Data Science, Statistics, Computer Science, or related field

Tools

Tableau

Power BI

Job description

Role overview

A remote, full-time W2 opportunity for an early-career analyst to manage, explore, and interpret healthcare datasets that underpin a virtual medical practice. The work spans data engineering, statistical analysis, and applied machine learning, partnering with product, engineering, and clinical stakeholders to surface insights that improve workflows and patient outcomes.

Responsibilities

• Collect, clean, and preprocess healthcare-related data from multiple sources to ensure reliable inputs for downstream analysis.
• Run statistical analyses and translate findings into actionable recommendations for cross-functional teams.
• Build and maintain dashboards, reports, and visualizations that communicate trends clearly to non-technical audiences.
• Collaborate with clinical, product, and engineering counterparts to scope data requirements and deliver fit-for-purpose solutions.
• Validate and safeguard data accuracy, completeness, and integrity through established quality practices.
• Develop and test entry-level machine learning models to surface patterns and support predictive use cases.

Requirements

• Bachelor's degree in Data Science, Statistics, Computer Science, or a closely related quantitative field.
• 1-2 years of experience in a data-focused role, including internships or comparable project work.
• Proficiency with SQL and at least one of Python or R for data analysis.
• Familiarity with visualization platforms such as Tableau or Power BI.
• Working knowledge of statistical methods and introductory predictive modeling concepts.
• Clear written and verbal communication, attention to detail, and a strong learning mindset.

Nice to have

• Exposure to healthcare data standards or formats such as FHIR or ICD-10.

Benefits and work setup

• Fully remote within the United States; candidates must already hold unrestricted authorization to work in the U.S. without future visa sponsorship.
• Hourly compensation of $35 per hour on a W2 basis.
• Required home-office setup: Windows 10 or later, or macOS 14 Sonoma; Intel Core i5 or Apple Silicon processor; 16 GB of RAM; wired headphones with no built-in microphone; a webcam; and 100 Mbps download / 50 Mbps upload wired internet connected via Ethernet. Work is performed through Amazon Workspaces accessed through Google Chrome, with the expectation that video may be on at any time.