DATA SCIENTIST - Automation testing

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

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