SAS to Python / R Migration Architect

The SAS to Python/R Migration Architect is responsible for end-to-end strategy, design, and governance of large-scale analytical and statistical code migrations from SAS to modern open-source ecosystems (Python and R). This role focuses on assessment, architecture, standards, risk management, and validation , working closely with stakeholders and development teams to ensure accuracy, performance, and regulatory fidelity.

This is a hands-on technical leadership role , not just documentation or oversight.

Key Responsibilities

• Lead enterprise-scale migrations from SAS (Base SAS, PROC SQL, STAT, ETS, MACRO, etc.) to Python and/or R
• Perform detailed SAS estate assessments , including:
• Code inventory and dependency mapping
• Macro complexity analysis
• Data access patterns (SAS datasets, DBs, flat files)
• Statistical method equivalency analysis
• Define target-state architecture for Python/R analytics platforms (libraries, frameworks, environments)
• Establish migration patterns and standards , including:
• SAS PROC → Python/R library mappings
• Macro-to-function translation strategies
• Reusable templates and shared components
• Design validation and reconciliation frameworks to ensure:
• Statistical equivalence
• Numeric tolerances
• Regulatory and audit compliance
• Guide performance optimization strategies for large datasets
• Identify automation opportunities (code scanners, translators, test harnesses)
• Lead technical reviews and approve migrated code
• Mentor developers and review complex conversions
• Communicate migration risks, tradeoffs, and timelines to leadership

Requirements

• 8+ years of advanced analytics or statistical programming experience
• 5+ years hands-on SAS development (Base SAS, PROC SQL, MACRO, STAT)
• Proven experience architecting or leading SAS → Python and/or R migrations
• Deep expertise in:
• Python (NumPy, Pandas, SciPy, statsmodels, scikit-learn)
• and/or R (tidyverse, data.table, caret, survival, forecast)
• Strong understanding of statistical methods parity between SAS and open-source tools
• Experience with data platforms (SQL databases, cloud storage, data lakes)
• Familiarity with CI/CD, version control, and testing frameworks for analytics code

Nice to Have

• Experience in regulated environments (government, healthcare, finance)
• Prior work modernizing legacy analytics platforms
• Exposure to cloud analytics stacks (AWS, Azure, GCP)
• Experience designing automated validation frameworks

Benefits

• 401(k)
• 401(k) matching
• Dental insurance
• Flexible spending account
• Health insurance
• Life insurance
• Paid time off
• Professional development assistance
• Referral program
• Tuition reimbursement
• Vision insurance