Data Scientist

7 months ago


Centurion, South Africa Momentum Insure Company Limited Full time

**Introduction**

Through our client-facing brands Metropolitan and Momentum, with Multiply (wellness and rewards programme), and our other specialist brands, including Guardrisk and Eris Property Group, the group enables business and people from all walks of life to achieve their financial goals and life aspirations.

**Role Purpose**

Create and deliver data-driven solutions that add business value through the use of statistical models, machine learning algorithms, data mining and visualisation techniques.

**Requirements**:
**Experience & Educational Requirements**:

- Degree in Computer Science, Computer Engineering, Actuarial Science, Statistics, Business Sciences, Business Mathematics. Other degrees or postgraduate qualifications with a statistical/modelling aspect and/or experience will be considered.
- A minimum of 4 years relevant experience in data science
- Proficiency in programming languages such as SQL and Python
- In-depth knowledge of machine learning modelling libraries (e.g., scikit-learn, PyTorch, Tensorflow)
- Experience with deep learning and natural language processing will be advantageous
- 2-5 years’ data analysis experience within an insurance environment
- Familiarity with visualisation tools (e.g. Tableau, PowerBI, Plotly)
- Experience in designing and building production systems in AWS, Google Cloud, or Azure
- Exposure to continuous integration/continuous deployment (CI/CD)
- Familiarity with version control systems (e.g., Git or AWS Commit)
- Experience in the short-term insurance industry will be advantageous

**Duties & Responsibilities**

INTERNAL PROCESS
- Identify and develop predictive and prescriptive models to enable better decision making of business.
- Identify, understand and interpret data structures across various databases within the business to facilitate data analysis and continuous monitoring of activities.
- Delve for insights in data and processes to help improve the business.
- Participate and/or lead discovery processes with business stakeholders to identify problems and opportunities that may be addressed with statistical modelling or machine learning.
- Identify and develop the hypothesis testing framework and modelling approach to address the business requirements.
- Make strategic recommendations on data collection and experimental design incorporating business requirements and knowledge of best practices.
- Prepare the data for analysis and modelling, which includes data cleaning, standardisation, transformation, dimension reduction and feature engineering.
- Identify and train suitable models/algorithms to discover patterns and make predictions.
- Extend existing code and develop custom code to implement statistical models, machine learning algorithms and data mining techniques for large datasets in a computationally efficient manner.
- Compare model performance, select the best algorithm and be able to motivate this choice in a non-technical manner.
- Interpret results and translate findings into clear and actionable insights that can be easily validated with the project sponsor.
- Communicate findings to business with various skill levels and in various roles, presenting trends, correlations and patterns found in complicated datasets in a manner that clearly and concisely conveys meaningful insights.
- Assist business users in the use of the models and interpretation of model output.
- Deploy ML models to production on cloud platforms such as AWS, Google Cloud, or Azure.
- Assist with monitoring and reporting on model accuracy after it has been embedded in operations.

PEOPLE
- Develop and maintain productive and collaborative working relationships with peers and stakeholders.
- Positively influence and participate in change initiatives.
- Continuously develop ones’ own expertise in terms of professional, industry and legislation knowledge.
- Contribute to continuous innovation through the development, sharing and implementation of new ideas.

**Competencies**
- Deep theoretical understanding of statistical methods and machine learning techniques
- Understanding of Artificial Intelligence solutions and techniques
Formulating business problems to enable statistical modelling
- Selecting the right statistical tools and techniques for the job
- Experience translating statistical findings into business recommendations
- Provide insights using visualisation tools (e.g. Tableau, PowerBI, Plotly)


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