Full-time
Posted 8 days ago
Job description
Our company is a start-up that specializes in episodes of care benefit plans. We have a deep expertise in episode pricing and analytics and our goal is to bring transparency and efficiency to the healthcare market. Our company is committed to providing high-quality, affordable healthcare to our customers.
Role Description
We are looking for a Data Analyst to join our team. In this role, you will be responsible for supporting our episode pricing and analytics operations, both to internal stakeholders as well as external customers. You will develop reports and visualizations that clearly communicate data-driven insights.
Responsibilities
- Analyze large amounts of healthcare data from multiple sources to support business decision making
- Perform exploratory data analyses and evaluate data quality and usability.
- Assist in the development and maintenance of ETL/ELT pipelines using established templates and internal tools.
- Perform routine data audits to flag quality issues, develop reports to monitor pricing quality and coverage, and assist in developing workarounds to data limitations.
- Assist in gathering data requirements by participating in stakeholder meetings and documenting business needs.
- Work closely with team members to identify key metrics and understand the "why" behind data requests.
- Document existing data workflows to help the team identify inefficiencies or error prone areas (available tools include Miro, Confluence, and Jira)
Qualifications
- Bachelor's degree ideally in a quantitative field, such as statistics, mathematics, economics, or computer science.
- 1-2 years of experience using Python (pandas/numpy) for data analysis, including composing robust data processing code to enforce expected patterns, handle exceptions, and evaluate data usability/quality through exploratory data analyses.
- Exposure to large-scale datasets; experience with healthcare claims or administrative data is a major plus but not required.
- Ability to perform descriptive analysis and translate findings into clear charts or summaries for the team.
- Excellent communication skills, with the ability to translate complex data into clear and actionable insights.
- Strong organizational skills, with the ability to work on multiple projects and priorities in a fast-paced environment.
- Basic familiarity with Git for version control (e.g., branching, committing, pulling code, and submitting pull requests)
Preferred Qualifications:
- Knowledge of a BI tool (i.e., Looker, PowerBI)
- Knowledge of basic statistical concepts and models (e.g. OLS, sampling variability, conditional probability, outlier detection, etc.)
- Experience using SQL databases and Python packages (pandas, sqlalchemy, multiprocessing)
- Experience developing Python utility packages for use across multiple scripts, chunking large datasets for efficient processing, and using abstraction to refactor complex processes
- Knowledge of healthcare markets, specifically with respect to value-based care
- Familiarity with AI-assisted coding tools such as Codex CLI, Cursor, Claude Code, etc.
Frequently asked questions
Q: What skills or qualities help someone succeed as a Data Analyst?
A: To succeed as a Data Analyst, key technical skills include proficiency in programming languages such as Python or R, expertise in data visualization tools like Tableau or Power BI, and knowledge of statistical analysis and machine learning concepts. Additionally, strong soft skills like effective communication, problem-solving, and collaboration are crucial for presenting insights to stakeholders and working with cross-functional teams. By combining these technical and soft skills, Data Analysts can drive business decisions, identify areas for improvement, and contribute to the growth and success of their organization.
Q: What is the career path for a Data Analyst?
A: A Data Analyst's typical career progression involves starting as an Entry-Level Data Analyst, where they collect, analyze, and interpret data to inform business decisions. As they gain experience, they can move into Mid-Level roles such as Senior Data Analyst or Business Analyst, where they take on more complex projects and lead smaller teams. Ultimately, they can advance to Senior Leadership positions like Data Scientist, Data Manager, or even Director of Analytics, where they oversee large-scale data initiatives and drive strategic business growth.\n\nKey opportunities for skill development and professional growth in this role include learning programming languages like Python or R, mastering data visualization tools like Tableau or Power BI, and staying up-to-date with emerging trends in machine learning and artificial intelligence. Additionally, Data Analysts can develop soft skills like communication, project management, and leadership to excel in their roles.\n\nLong-term career prospects for Data Analysts are diverse, with potential directions including transitioning into related fields like Business Intelligence, Data Engineering, or even becoming a Product Manager, or pursuing advanced degrees in Data Science or related fields to further specialize in areas like machine learning or data engineering.
