Data Scientist, SAS-to-Python Migration (Expert)

Daily Responsibilities:

• Code Conversion: Translate existing SAS scripts into Python for critical deliverables including IFRS9 Model, Risk Campaigns, and Reporting and Analytics. Ensure logic accuracy and functional equivalence, optimizing code for performance and maintainability in the Python environment
• Parallel Validation: Execute simultaneous runs of legacy SAS and new Python processes to verify identical outputs and mitigate transition risks.
• Data Mining & Statistical Modeling: Leverage advanced data mining techniques and statistical modeling methodologies to extract, analyze, and validate complex datasets within the IFRS9 and Risk Management domains
• Data Lineage & Compliance: Create comprehensive data dictionary and establish end-to-end data lineage, documenting data origin, transformation, and movement throughout its lifecycle to support regulatory compliance and auditability.
• Training and Knowledge Transfer: Conduct structured training sessions with permanent staff to ensure seamless sustainment.

Must-have Skills:

• 10 + years of experience in SAS migration, data migration, with preference for candidates from financial institutions.
• Advanced SAS and Python expertise, with proven SAS-to-Python migration experience
• Proficiency in data profiling, validation, and quality assurance
• Data lineage mapping and data dictionary development expertise
• Strong understanding of IFRS9 modeling and credit risk analytics
• Parallel testing and validation capabilities
• ​Excellent technical documentation and communication abilities
• Proven ability to work with teams and deliver on time
• Post-conversion support and maintenance capability

Nice-to-Have Skills:

• Financial institution background is preferred

Soft Skills:

• Excellent technical documentation and communication abilities
• Proven ability to work with teams and deliver on time
• Post-conversion support and maintenance capability

Education:

• Bachelor’s Degree
• Master’s degree or equivalent in statistical background (strongly preferred)