QA Data Scientist

Quality Assurance Data Scientist Key Responsibilities Data Science & AI Support • Develop, analyze, and validate machine learning models used in AI applications. • Perform data exploration, feature engineering, and model evaluation to support AI initiatives. • Partner with AI engineers to assess model accuracy, bias, drift, robustness, and explainability. • Design metrics and dashboards to track model performance and data quality over time. Collaboration with QA Automation • Work closely with Senior QA Automation Engineers to: • Define AI and data validation strategies • Create test datasets (synthetic, edge-case, adversarial) • Support automated testing of ML pipelines and AI models • Assist in testing scenarios such as: • Model retraining validation • Regression testing for AI outputs • Data drift and concept drift detection • AI fairness, bias, and ethical testing Databricks & Data Engineering • Build and maintain data pipelines using Databricks (Spark, Delta Lake). • Write optimized SQL, PySpark, and notebooks for data processing and analysis. • Collaborate with data engineers to ensure scalable, secure, and reliable data workflows. • Implement best practices for data versioning, lineage, and reproducibility. AI Quality, Governance & Security • Support AI readiness and quality frameworks, including model validation and auditability. • Assist with AI governance, documentation, and compliance needs. • Contribute to AI risk assessments, including model explainability and failure analysis. • Work with security and QA teams on AI red-teaming, adversarial testing, and jailbreak scenarios (where applicable). Required Qualifications • Bachelor’s or Master’s degree in Data Science, Computer Science, Engineering, Statistics, or related field. • 3+ years of experience as a Data Scientist or similar role. • Strong hands-on experience with Databricks (Spark, Delta Lake, notebooks). • Proficiency in Python, SQL, and PySpark. • Experience working with machine learning models(training, evaluation, monitoring). • Solid understanding of data quality, validation, and testing concepts. • Experience collaborating with QA or Automation Engineering teams. Preferred Qualifications • Experience supporting AI/ML testing or MLOps. • Familiarity with model monitoring, drift detection, and ML lifecycle tools. • Knowledge of AI ethics, bias detection, and explainability (XAI). • Exposure to cloud platforms (Azure preferred, especially Azure Databricks). • Understanding of CI/CD pipelines for data and ML workflows. • Experience with GenAI, LLMs, or AI security testing is a plus. Soft Skills • Strong collaboration skills across data, QA, and engineering teams. • Ability to translate complex data insights into clear, actionable outcomes. • Detail-oriented with a quality-first mindset. • Comfortable working in fast-paced, AI-driven environments. #Remote