Machine Learning QA Engineer

Qualitest looking for of software tester with the creativity of data science to ensure the accuracy, reliability, and performance of our data-driven products. You'll design automated testing frameworks, validate machine learning models, and collaborate with cross-functional teams to uphold the highest standards of data quality and model integrity.

Key Responsibilities:

• Develop and maintain automated testing frameworks for data pipelines and machine learning models.
• Design and execute test cases to validate statistical models, algorithms, and data transformations.
• Monitor data quality, detect anomalies, and ensure consistency across datasets.
• Collaborate with data scientists, engineers, and QA teams to define test strategies and acceptance criteria.
• Perform exploratory data analysis to uncover hidden issues in data or model behavior.
• Leverage real world data and build synthetic datasets to simulate edge cases, stress-test models, ensure unbiased predictions, and verify data security
• Coordinate with end users to run human in the loop and A/B tests
• Document test results, bugs, and performance metrics to support continuous improvement

Required Qualifications:

• Bachelor's or Master's degree in Computer Science, Data Science, Statistics, or a related field.
• 3+ years of experience in data science and AI/ML testing
• Proficiency in Python, SQL, and testing frameworks (e.g., PyTest, unittest).
• Experience with machine learning libraries (e.g., scikit-learn, TensorFlow, XGBoost).
• Strong understanding of statistical testing, model validation, and data integrity principles.
• Familiarity with CI/CD pipelines and version control (e.g., Git, Jenkins).

Preferred Skills:

• Experience using Oracle AI Data Platform / Oracle Cloud Infrastructure (OCI) including Medallion architecture
• Strong mastery of SQL
• Knowledge of MLOps and model monitoring tools
• Familiarity with Azure Dev Ops (ADO) for test management
• Excellent communication and documentation skills.

Top 3 Must Haves:

Proficiency in Python, SQL, and testing frameworks (e.g., PyTest, unittest).

Experience with machine learning libraries (e.g., scikit-learn, TensorFlow, XGBoost).

Strong understanding of statistical testing, model validation, and data integrity principles.