Job Description
Job Description - Data Science Specialist (2100007H)
Job Description
Data Science Specialist - (2100007H)
Job Purpose and Key responsibilities
Job Purpose
The role holder will drive Data Analytics within the Group with an aim of developing customer centric solutions.
Key responsibilities
1. Work with stakeholders throughout the Group to identify customer insights including:
• 360-degree view of customers
• Upselling opportunities
• Cross-selling opportunities
• Lead generation and management
• Digital marketing optimization
2. Leverage Data Science to drive innovative customer solutions with a special focus on:
• Pricing optimization
• Discount policy management
• Portfolio monitoring and management
• Churn prevention
3. Leverage Data Science to optimize claims and operational processes with a special focus on:
• Fraud prevention and Back office optimization
• AI to support claims management
4. Assess the effectiveness and accuracy of new data sources and data gathering techniques.
5. Coordinate with different functional teams to implement models and monitor outcomes.
6. Develop dashboards and tools to monitor and analyze model performance and data accuracy.
7. Any other duties assigned by line manager.
Knowledge, experience and qualifications required
Knowledge, Experience and Qualifications required
1. University Degree in Data Science, Statistics, Actuarial Science – or other highly numerate subject area.
2. At least 2 years of data science experience in an e-commerce, digital marketplace and/or financial services.
3. Proven experience working across functions and with multiple stakeholders.
4. A keen eye for innovation in the insurance and financial services space preferred.
5. Experience in creating and using advanced machine learning algorithms and statistics: regression,
6. simulation, scenario analysis, modelling, clustering, decision trees, etc.
7. Coding knowledge and experience with several languages.
8. Experience querying databases and using statistical computer languages: R, Python, SLQ, etc.
9. Experience using web services: Redshift, etc.
10. Experience analyzing data from 3rd party providers: Google Analytics, Facebook Insights, etc.
11. Experience with distributed data/computing tools: Hadoop, MySQL, etc.
12. Experience visualizing/presenting data for stakeholders using: Power BI, Data Studio etc.
13. Experience working with and creating data architectures.
14. Experience in data mining.
15. Knowledge of advanced statistical techniques and concepts (regression, properties of distributions,
16. statistical tests and proper usage, etc.) and experience with applications.
17. A drive to learn and master new technologies and techniques.
Key Competencies
• Excellent oral and written communication skills with the ability to translate and communicate complex principles to a non-technical audience.
• Ability to build and maintain effective working relationships with all stakeholders within each client segment.
• Ability to think clearly and analytically.
• Ability to identify and solve all problem types i.e. both technical and non-technical.
• A keen eye for innovation in the insurance and financial services both traditional and non-traditional (FinTechs etc.) spaces.
• Post graduate degree/qualifications in data analytics or data science (will be an added advantage).
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