SAS to Python / R Migration Architect

Ignite•Suitland-Silver Hill, MD

11d

About The Position

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.

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 Haves

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

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