Data ScientistSantam Insurance

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  Involvement in engagement with stakeholders to understand their business challenges and advise on practical analytics solutions 
• Collaborate with key stakeholders within the data value chain, external and internal, to ensure that the appropriated data sources and data structures are in place for building analytics solutions
• Research, develop and implement appropriate statistical / mathematical / machine learning models as needed
• Keep up-to-date with latest technology trends
• Communicate results and ideas to key decision makers
• To generate and maintain actuarial data assets and make these available to the rest of the organization 
• Develop and maintain outstanding data from the Analytical Base Table and to utilize this to develop predictive models, assist other Business Units in doing diagnostic and descriptive analytics
• Ensure automation of data assets delivered timeously
• Use advanced analytics techniques to solve business problems in cooperation with business units
• Seek out initiatives to enhance Santam’s capability in the Data Science field
• Assist users across the group to make use of the data science workbench

Qualifications and Experience

• B. Degree in Quantitative Management (decision sciences) or Computer Science/Statistics/Applied Statistics/Applied Mathematics (Postgraduate preferable)
• 5-7 years practical experience in an analytical/quantitative environment

Skills

• Strong programming skills ( SQL essential, Python and/or R highly desirable, Spark, Java )
• Experience with common data science toolkits, such as R, Hadoop, Scala, RapidMiner, Alteryx, SAS, SPSS etc. highly desirable
• Experience in Data integration
• Machine learning / Artificial Intelligence and Statistical algorithm development essential 
• Experience with Big Data platforms highly desirable
• Experience in Data management and integration such as Ralph-Kimbal dimensional modelling (Star-schema model) or Bill Inmon Snow-flaking model essential
• Business acumen: Enterprising & commercial thinking: Keeps aware of corporate markets and the state of competitors, identifies business opportunities, views issues in terms of costs, profits, markets and added value.    
• Statistical analysis: Data scientists must have a strong foundation in statistical analysis to effectively identify patterns, trends, and relationships within data.
• Programming: Proficiency in programming languages such as Python and R is essential for data scientists to analyse data and build predictive models.
• Data wrangling: Data scientists must be able to gather, clean, and pre-process data to ensure its accuracy and reliability.
• Machine learning: Knowledge of machine learning algorithms and techniques is necessary to build predictive models and make data-driven decisions.
• Data visualization: Data scientists should have the ability to create clear and effective visualizations to communicate insights and findings to stakeholders.
• Communication
• Problem-solving
• Creativity
• Curiosity
• Financial acumen

Competencies

• Client Focus
• Collaborates
• Cultivates Innovation
• Drives results
• Flexible and adaptable
• IT Data Analysis
• Data Collection
• Advanced analytics to address business requirements
• New technologies and methodologies
• Stakeholder management