The Data Analyst plays a crucial role in the project team, responsible for analyzing healthcare data, creating meaningful visualizations, generating insights, and supporting data-driven decision-making and public health research. This role focuses on cleaning, preprocessing, and analyzing data to uncover patterns, trends, and key metrics that contribute to achieving project objectives.
Responsibilities:
Data Preprocessing: Clean and preprocess large-scale healthcare datasets, ensuring data quality, consistency, and readiness for analysis. Address missing values, outliers, and other data anomalies to prepare the data for meaningful insights.
Data Analysis: Conduct rigorous data analysis using statistical techniques and tools. Identify patterns, trends, correlations, and other meaningful insights in healthcare data to inform decision-making and research.
Health Outcomes Analysis: Collaborate with domain experts to analyze health outcomes, disease incidence, prevalence, severity, and other relevant metrics. Frame analyses within the context of public health objectives.
Data Visualization: Create informative and visually appealing data visualizations to communicate findings to both technical and non-technical stakeholders. Use visualization tools (e.g., Tableau, ggplot) to convey complex data patterns in a clear and understandable manner.
Report Generation: Generate regular and ad hoc reports for internal and external stakeholders, summarizing analysis results, key findings, and recommendations. Communicate insights through clear and concise reports and presentations.
Collaboration with Data Team: Work closely with data scientists, data engineers, and other members of the project team to understand data requirements, support statistical modeling efforts, and ensure alignment with project goals.
Health Equity-Focused Analyses: Contribute to the design and execution of health equity-focused studies, analyzing disparities and vulnerabilities in disease patterns and outcomes among specific population groups.
Data Quality Assurance: Implement data quality checks, validation processes, and data cleansing as needed to ensure the accuracy and reliability of healthcare data.
Continuous Learning: Stay updated on the latest data analysis techniques, tools, and best practices. Incorporate new knowledge into analyses to enhance the depth and quality of insights.
Qualifications:
Bachelor's or Master's degree in Data Science, Statistics, Epidemiology, or a related field.
Proven experience (typically 2+ years) in data analysis roles, preferably with exposure to healthcare or large-scale data projects.
Proficiency in data preprocessing, statistical analysis, and data visualization.
Strong programming skills in languages such as R or Python for data analysis.
Experience with data visualization tools, such as Tableau or ggplot, to create meaningful and informative visualizations.
Understanding of public health concepts and health outcomes.
Excellent communication skills, capable of conveying analysis findings to both technical and non-technical audiences.
Ability to collaborate effectively with cross-functional teams, domain experts, and stakeholders.
Ethical and trustworthy, with a commitment to maintaining the highest level of integrity in data analysis.
Knowledge of data privacy regulations (e.g., HIPAA) and ethical considerations in data handling.
Infiuss Health
