Analytics
Skills
About This Role
Core Responsibilities
and Accountabilities
1. Non-technical
- Strong communication skills and ability to present findings to client stakeholders
- Collaborate with cross-functional teams to understand business requirements
- Document all steps in the development process
2. Technical
- Advanced knowledge of statistical analysis and machine learning
- Highly Proficient in SAS programming language (SAS 9.4)
- Proficient in other Data Science programming languages (e.g. Python, R)
- Develop, test, implement, and maintain predictive models
- Segmentation, customer lifetime value
- Identify opportunities to innovatively use data/ extract insights from complex data sets
3. Cultural Fit
- Owning the outcome (ownership)
- Being driven/passionate in analytics
- Problem solving mindset, challenging the status quo
- Self-organizing, independent and takes ownership of each task
- Meticulous to detail
- Thrive under pressure
KPIs
- Time to deployment & value identified/realized/recommended from different use cases.
- Model accuracy (e.g., RMSE, MAE for regression tasks)
- Number of business problems addressed
- Responsiveness to business questions/problems/requests
- Number of departments/business users supported/adopted the solution Stakeholder Satisfaction: The level of satisfaction of stakeholders with the reports and insights provided.
- Collaboration Effectiveness: The effectiveness of collaboration with cross-functional teams, measured through the quality of insights extracted.
- Data Security Compliance: Compliance with data security protocols and regulations
- Knowledge and Experience
- 10years + in a similar role, ideally with financial services
Skills
- Advanced knowledge of statistical analysis and machine learning
- Highly Proficient in SAS programming language (SAS 9.4)
- Proficient in other Data Science programming languages (e.g. Python, R)
- Strong communication skills and ability to present findings to senior stakeholders (non-technical)
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