Location: United States (Remote / Hybrid)
Experience Level: 8 10 Years
Job Type: 3 Months contract with possible extension
Role Overview
Highly skilled and experienced **Lead/Senior Quantitative QA & Migration Engineer** with 8 to 10 years of professional experience to join our analytics modernization team. The ideal candidate will serve as a subject matter expert (SME) in SAS (particularly in data ingestion and statistical modeling pipelines) while possessing strong, hands-on capabilities in Python.
Play a critical part in our technology modernization initiative: migrating legacy statistical models and data pipelines from SAS to Python. You will collaborate closely with data scientists, model developers, and QA engineers to validate, compare, and reconcile the outputs of migrated Python models against legacy SAS baselines. Your deep understanding of statistical modeling and SAS data ingestion will be vital in identifying, debugging, and resolving discrepancies, ensuring absolute fidelity and compliance of the new Python-based models.
Key Responsibilities
1. Legacy SAS Expertise & Data Ingestion
* Act as the team s primary authority on legacy SAS pipelines, macro systems, and statistical models.
* Analyze, document, and reverse-engineer complex legacy SAS codebases used for large-scale data ingestion, ETL, and statistical modeling.
* Ensure that all data ingestion nuances, SAS formats, merging logic, and data manipulations are correctly understood and documented before migration.
2. Model Quality Assurance & Reconciliation
* Define the QA framework and tolerance criteria for comparing SAS and Python model outputs.
* Perform rigorous quantitative reconciliation: run legacy SAS models, run newly migrated Python models, and perform cell-by-cell and statistical comparison of outputs (e.g., comparing coefficients, predictions, confidence intervals, and data distributions).
* Identify, debug, and explain discrepancies between SAS and Python outputs (e.g., due to floating-point differences, sorting orders, treatment of missing values, or library implementation variations).
* Proactively modify, optimize, and debug the converted Python code to ensure alignment with legacy SAS behavior or to establish verified, superior Python methodologies.
3. Python Development & Code Modernization
* Review, refactor, and write clean, modular, and performant Python code using standard libraries (Pandas, NumPy, Scipy, Statsmodels, Scikit-Learn).
* Assist the development team in translating complex SAS Macros and procedural blocks (`PROC SQL`, `PROC TRANSPOSE`, `PROC REG`, `PROC LOGISTIC`, etc.) into idiomatic Python scripts/notebooks.
* Promote best practices in software development, including version control (Git), unit testing, and robust documentation.
Qualifications & Skills
Technical Expertise
* SAS (Expert level):
* Advanced SAS programming skills (Base SAS, SAS Macros, SAS/STAT, SAS/GRAPH).
* Extensive experience in SAS data ingestion techniques, handling large-scale datasets, optimizing data steps, and performing complex joins/merges.
* Strong familiarity with key SAS procedures used in statistical analysis and modeling.
* Python (Intermediate to Advanced):
* Strong capability in writing clean, readable, and efficient Python code.
* Proficiency with data manipulation and analysis libraries: Pandas and NumPy.
* Solid experience with statistical and modeling libraries: Statsmodels, SciPy, and Scikit-Learn.
* Understanding of Git workflows and code validation practices.
* Database & SQL: Strong SQL skills for querying databases, verifying source datasets, and data validation.
* Statistical Knowledge:** Good understanding of statistical modeling concepts, regression analysis, hypothesis testing, and model validation techniques.
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