Data Analyst at PPS

Minimum Requirements

• Matric
• Bachelor’s degree in Data Science, Statistics, Computer Science, or related field
• Data science programming languages such as R, Python.
• Data manipulation skills including SQL to extract, transform and load data.
• Experience working with PowerBI
• Knowledge of the medical scheme act and relevant legislation/regulations 
• Operating data mining tools and software and data analytical software (e.g ACL, Idea, Qlickview,  BI, Yellowfin etc.)
• Experience in interactive data exploration.
• Strong ability to communicate findings and recommendations from data (visual, verbal and written).
• Confident and professional manner
• Articulate, both verbally, writing and visual
• An objective, logical and enquiring mind
• Strong analytical and problem-solving skills
• Good project management and team working ability
• Good time management
• Must be able to work independently and under pressure
• High level of attention to detail, resilience, enthusiasm, energy and drive.
• Ability to work cohesively in a team environment and balance multiple priorities.

Duties and Responsibilities

• Collaborate with a multitude of stakeholders to translate data findings into actionable insights and strategic recommendations.
• Collect, analyze, and interpret large and complex healthcare datasets to identify trends, patterns, and insights related to medical scheme performance and fraud, waste and abuse.
• Develop and deliver regular reports on medical scheme metrics and fraud, waste and abuse. This includes but is not limited to retention models, utilization rates, cost analysis, patient demographics, and treatment outcomes.
• Ensure the accuracy, completeness, and reliability of the data used in analyses. Implement and maintain data governance and privacy standards, especially with sensitive patient information.
•  Continuously seek ways to improve medical scheme and fraud, waste and abuse reporting and analytics processes. This includes the adoption of new technologies, methodologies, and best practices in data science.