QA Data Scientist

Key Responsibilities

• 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.
• 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, and AI fairness, bias, and ethical testing.
• 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.
• 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).

• 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.

• 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.

• 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 position.

Mid‑Senior level

Contract

Engineering and Information Technology