Data Scientist

3 days ago

Johannesburg, South Africa Medical Placements Full time

EE Position:
**THE IDEAL CANDIDATE SHOULD HAVE**:

- Matric with either of the below qualification is essential.
- Honours degree in either Data Science, Statistics, or Applied Mathematics with some experience in software engineering, computer science or working with big disparate sets of data, OR
- Honours or Master’s degree in Computer Science or Software Engineering with solid experience in statistical modelling, data mining and machine learning.
- Other analytical qualifications will also be considered if accompanied by the relevant experience.
- Expert in data science programming languages such as R, Python.
- Expert in data manipulation skills including SQL to extract, transform and load data.
- Experience in interactive data exploration.
- Hands on experience with Big Data systems will be preferred.
- Strong analytical and statistical knowledge with an understanding of the latest machine learning algorithms for both structured and unstructured data.
- Ability to formulate problem statements and develop a plan for tackling the problem.
- Strong ability to communicate findings and recommendations from data (visual, verbal and written).
- Integration and implementation experience.
- A passion for data exploration and analytics.
- Self-starter.
- Willingness to learn and grow exponentially.
- A restless curiosity towards data and uncovering unknown correlations.
- Ability to work cohesively in a team environment and balance multiple priorities.
- A team player who can work alone when required and without supervision.
- High level of attention to detail, resilience, enthusiasm, energy and drive.
- Positive, can-do attitude.
- Ethical and able to maintain confidentiality and manage boundaries.

**Duties**:

- Connecting with a multitude of stakeholders to understand the data in a healthcare and fraud context.
- Mining large structured and unstructured data sets for data exploration to find new insights to detect healthcare fraud.
- Present data and model findings in a way that provides actionable insights.
- Implementing results for use in both the internal and external environments.
- Monitoring model performance.
- Improve processes and databases where opportunities arise.

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