Knowledge & Experience
3 years + in a similar role, ideally with financial services
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)
Experience in Dataiku and understanding of MLOps (on Azure)
Experience in financial industry will be an advantage
Strong communication skills and ability to present findings to senior stakeholders (non technical)
Requirements
Non-technical
Strong communication skills and ability to present findings
Experience in financial industry
Collaborate with cross-functional teams to understand business requirements
Document all steps in the development process
Stay updated with the latest developments in the field
Technical
Advanced knowledge of statistical analysis and machine learning
Highly proficient in SAS programming language (SAS 9.4)
Proficient is other data science programming languages (e.g. Python, R)
Experience in Dataiku and understanding of MLOps (on Azure)
Develop, test, implement, and maintain predictive models
Identify opportunities to innovatively use data⁄ extract insights from complex data sets
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
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.