QA Architect – R01571456

Job requirements

Experience Range: With at least 7 years of quality assurance experience, including substantial hands-on work with data science and machine learning testing frameworks Key Responsibilities:
• Design and implement automated testing strategies for AI and data science outputs, ensuring accuracy and reliability across models and pipelines
• Develop and maintain robust evaluation and validation frameworks for backend and frontend components, leveraging statistical and machine learning techniques
• Collaborate with data scientists and engineers to define test cases, hypotheses, and statistical metrics for model assessment and improvement
• Integrate advanced statistical tests such as T-Test, Z-Test, and regression analyses into automated QA workflows to validate model performance
• Utilize tools like Great Expectations, Evidently AI, and specific machine learning frameworks (TensorFlow, PyTorch, Sci-Kit Learn, CNTK, Keras, MXNet) to monitor, track, and report on model drift, anomalies, and forecast accuracy
• Optimize testing processes for scalability and efficiency using Python, PySpark, R, and related technologies in large-scale data environments
• Configure and manage testing infrastructure using platforms such as KubeFlow and BentoML to streamline deployment and evaluation cycles
• Troubleshoot and resolve issues in automated testing pipelines, driving continuous improvement and high-quality deliverables
Required Skills:
• Advanced proficiency in Python and PySpark for test automation and statistical analysis
• Expertise in statistical testing methods including Hypothesis Testing, T-Test, Z-Test, and Regression (Linear, Logistic)
• Strong experience with machine learning frameworks such as TensorFlow, PyTorch, Sci-Kit Learn, CNTK, Keras, and MXNet
• Hands-on knowledge of Great Expectations and Evidently AI for data validation and monitoring
• Proficiency in SAS and SPSS for statistical computing and analysis
• Deep understanding of probabilistic graph models and classification algorithms including Decision Trees and SVM
• Experience with forecasting techniques including Exponential Smoothing, ARIMA, and ARIMAX
• Familiarity with distance metrics such as Hamming, Euclidean, and Manhattan Distance
• Advanced skills in R and R Studio for statistical modeling and QA scripting
• Experience configuring testing platforms such as KubeFlow and BentoML
Preferred Skills:
• Experience automating evaluation pipelines for AI/ML in production environments
• Expertise in integrating QA processes with CI/CD workflows and cloud-native architectures
• Knowledge of emerging ML testing tools and frameworks beyond industry standards
• Ability to develop custom statistical metrics for model evaluation
• Experience with QA automation for distributed systems at scale
Desired Qualifications:
• Bachelor’s degree in Computer Science, Data Science, Statistics, Mathematics, or a quantitative discipline
• Certification in Quality Assurance, Data Science, or Machine Learning (e.g., ISTQB Advanced Test Analyst, TensorFlow Developer Certificate)
• Certification in statistical analysis tools or platforms (e.g., SAS Certified Specialist, SPSS Certification)