Data Scientist - Centurion at Momentum Metropolitan Holdings Limited

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
• Ability to apply statistical methods and machine learning techniques to solve business problems
• 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)