Role- AI/ML Test Engineer (Data Science Focused)
Location- US Remote - MST hours
Job description:
This hybrid role combines the rigor of software testing 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
Familiarity with CI/CD pipelines and version control (e.g., Git, Jenkins).
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.
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.
They need to be well versed in both QE and AI/ML since they'll be the main POC between the QE team and the Data Science team.