Data Analyst I

Location :  Remote

Reports to :  Manager of Integrations

Department :  Implementation

About PatientIQ

PatientIQ is a fast-growing healthcare technology company on a mission to improve patient outcomes through smarter, data-driven care. We work with healthcare providers, life sciences organizations, and research institutions to transform clinical insights into action. As we scale, we are investing in strong operational infrastructure to enable consistent, predictable growth across the commercial organization.

Role Overview

Are you interested in building the future of healthcare and transforming the patient experience? Are you hopeful about what data and medical research can do to improve medicine? We’re looking for a Data Analyst to ensure PatientIQ remains at the forefront of using data to drive positive healthcare outcomes.

As a core member of the Data Engineering department, you will be in a dynamic environment that works cross-functionally with all other departments, such as Engineering, Product, Customer Success, and Marketing. You will work on a broad array of problems that rely on data to derive insights for our business and customers. We heavily value diligence, curiosity, and initiative, which are key to unlocking the value of PatientIQ's data for our users and decision-making. Your work will be impactful across the entire organization.

What You’ll Do

• Developing analytic solutions for internal teams and external customers on topics such as business development, patient engagement, patient-reported outcomes, and performance intelligence.
• Collecting, cleaning, and organizing large datasets from a variety of sources.
• Developing and implementing statistical models to uncover trends and patterns in the data.
• Communicating findings and insights to stakeholders through clear and compelling visualizations and reports using tools such as Metabase.
• Collaborating with cross-functional teams to identify opportunities for data-driven decision-making and innovation.
• Contributing to existing data pipelines through feedback or directly to ensure the accuracy and reliability of our data.
• Helping to identify and implement new data sources to support our analysis efforts.
• Assist the customer success teams with ad-hoc data and reporting requests from the customers.

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