Sobre a vaga

QA Architect 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)