The company is looking to solve the problem of ensuring the accuracy, reliability, and performance of their data-driven products by combining the rigor of software testing with the creativity of data science.
Requirements
- 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).
- Experience using Oracle AI Data Platform / Oracle Cloud Infrastructure (OCI) including Medallion architecture
- Knowledge of MLOps and model monitoring tools
- Familiarity with Azure Dev Ops (ADO) for test management
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
Other
- 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
- Excellent communication and documentation skills.
- Ability to work as the main POC between the QE team and the Data Science team.
- FTE Only, US Remote - MST hours
