Data Scientist

MediCard Phils., Inc.is one of the country's leading HMOand the only HMOfounded and run by Doctors. Since its inception, the concept of service-oriented total health care has been the molding ideal of MediCard. The competition is vast, and the benefits being offered by the competitors are tempting. However, MEDICard has taken the lead in providing innovative and productive ideas that cut down the cost of health maintenance without compromising its quality.

MediCardnow boasts of more than half a million members and over 54,000 accredited doctors in over 1,000 hospitals and clinics nationwide. It also operates 16 MediCard free-standing clinics that provide services at par with those offered by hospitals minus the confinement.

MediCardis currentlylooking for assertive, dynamic and energetic individuals to fill up the following vacancy:

To apply advanced analytics and data science methodologies in the analysis of healthcare data, utilizing tools such as SAS, SQL, Python, R, Power BI, and modern platforms like Databricks. The goal is to uncover actionable insights that inform strategic decision-making, enhance risk assessment, and optimize costs. This role emphasizes the development of predictive models for claims forecasting, fraud detection, member segmentation, and utilization analysis. The Data Scientist will work collaboratively across business units to deliver value-driven insights and support value-based care initiatives aimed at improving utilization rates and overall healthcare outcomes.

Data Gathering & Research
1. Design and implement data collection strategies from internal systems and external sources.
2. Conduct exploratory research to identify relevant datasets and emerging trends in healthcare analytics.
3. Collaborate with cross-functional teams to define data requirements and ensure data quality and integrity.

Data Analysis & Modeling
Analyze complex healthcare datasets to identify trends, patterns, and anomalies. Develop and validate predictive models for claims forecasting, fraud detection, member segmentation, and utilization analysis. Apply statistical and machine learning techniques to support data-driven decision-making.

Tool & Platform Utilization
Leverage tools such as SAS, SQL, Python, R, Power BI, and Databricks to process, visualize, and interpret data. Automate data workflows and reporting processes to improve efficiency and scalability.

Business Collaboration
Partner with various business units (BAUs) teams to understand analytical needs and translate them into actionable insights. Present findings and recommendations to stakeholders through clear visualizations and reports.

Strategic Support
Contribute to value-based care initiatives by providing insights that improve utilization rates and healthcare outcomes. Support risk assessment and cost optimization strategies through data-driven analysis.

Quality & Compliance
Ensure data integrity, accuracy, and compliance with healthcare regulations and standards. Maintain documentation of methodologies, models, and processes for transparency and reproducibility.