About the role
2 Key Responsibilities
Perform end-to-end QA of data science pipelines and analytics platforms
Validate data ingestion, transformations, and feature engineering flows
Conduct model validation, performance benchmarking, and sanity checks
Identify data leakage, bias, drift, and inconsistencies
Design and execute platform-level testing (functional, data, and integration testing)
Validate cross-platform outputs and consistency of analytics results
Develop automated QA frameworks, validation scripts, and test cases
Perform regression testing for model and pipeline updates
Work with DS/Engineering teams to debug, fix, and improve system reliability
Maintain QA documentation, test coverage, and audit logs
3 Required Skills
Strong Python (Pandas, NumPy, Scikit-learn)
Good understanding of ML lifecycle and evaluation metrics
Experience in:
o Model validation / analytics QA
o Data pipeline testing (ETL validation)
o Platform/system testing
Strong SQL skills for data validation
Experience creating test cases, validation frameworks, and automation
4 Good to Have
Exposure to MLOps / model monitoring / drift detection
Experience with testing frameworks (PyTest, Great Expectations)
Knowledge of APIs, microservices testing
Familiarity with cloud platforms (AWS/Azure)
Understanding of CI/CD for data and ML pipelines
Disclaimer: This job posting has been aggregated from external source. Role details, content, and availability are subject to change. Applicants are advised to confirm the latest information directly on the company website before applying.
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