1. Data Mining & Predictive Analytics
- Analyze large-scale healthcare datasets, including EHRs, claims data, and operational databases.
- Apply machine learning algorithms and statistical methods to identify trends, anomalies, and patterns related to patient outcomes and healthcare delivery.
- Develop predictive models to forecast health risks and treatment responses for proactive care planning.
- Use tools such as Python (Pandas, NumPy, Scikit-learn, TensorFlow), SQL, Hadoop, Spark, and NoSQL databases.
2. Data Slicing, Dicing & OLAP Analysis
- Perform data segmentation based on demographics, conditions, or treatment histories to support targeted analysis.
- Conduct OLAP operations like drill-down, slicing and dicing to explore multi-dimensional views of healthcare data.
- Utilize tools such as SQL, Excel, Power BI, Tableau, and Python libraries (Matplotlib, Seaborn).
3. Data Visualization & Reporting
- Create intuitive dashboards and reports that communicate KPIs, patient outcomes, and performance metrics to various stakeholders.
- Design and develop visual representations (bar charts, heatmaps, flow diagrams) to make complex data accessible and actionable.
- Ensure clarity, accuracy, and transparency in all visual communications using tools like Power BI, Tableau, D3.js, Matplotlib, and Seaborn.
Example Datasets Youll Work With:
- Clinical Data: EHRs, medication records, patient history.
- Operational Data: Facility management, staff allocation.
- Financial Data: Insurance claims, billing, cost metrics.
- Outcome Data: Patient satisfaction, readmission rates, treatment efficacy.
Qualifications:
- Bachelors or Masters in Computer Science, Data Science, Health Informatics, Biostatistics, or a related field.
- 3–6 years of experience in healthcare data analytics or a related domain.
- Proficiency in SQL, Python, and data visualization platforms (Power BI, Tableau).
- Experience working with large healthcare datasets and machine learning models.
- Strong understanding of healthcare operations and data privacy standards (e.g., HIPAA)
