1. End-to-end Data Analysis & Reporting
a. Preparation: Clean, preprocess, and transform structured and semi-structured
healthcare data from multiple sources (EHRs, registries, surveys). Support the
development and maintenance of reproducible data pipelines and analysis
workflows.
b. Analysis: Analyze clinical, administrative, and operational datasets related to identify
trends, patterns, and anomalies.
c. Visualization & Reporting: Develop clear and informative dashboards, reports, and
visualizations to communicate findings to clinical, administrative, and research
stakeholders.
2. Data Monitoring & Engineering
a. Manage and query databases to support analytics and reporting needs.
b. Assist in maintaining and optimizing data pipelines and analytics datasets.
c. Work with data platforms and databases such as Oracle and cloud-based systems
3. Statistical Modeling & Machine Learning
a. Assist in building, validating, and interpreting statistical and machine learning
models for outcomes prediction, risk stratification, and operational optimization.
b. Apply appropriate evaluation metrics and ensure model results are interpretable and
clinically meaningful.
4. Research & Academic Support
a. Collaborate with clinicians, researchers, and senior analysts on study design,
methodology selection, and data analysis for research projects.
b. Contribute to abstracts, reports, and manuscripts by supporting results generation
and methodological documentation.
5. Data Quality, Ethics & Compliance
a. Ensure data accuracy, consistency, and completeness across analyses.
b. Adhere to data governance, privacy, and ethical standards relevant to healthcare and
research environments.
6. Stakeholder Collaboration
a. Work closely with multidisciplinary teams to understand analytical needs and refine
problem statements.
b. Present findings to non-technical audiences with guidance from senior team
members.