Data Science & AI

We are seeking a Data Science & AI Support Engineer to support enterprise AI initiatives by developing, validating, and monitoring machine learning models while partnering closely with AI Engineering, QA Automation, and Data Engineering teams. This role focuses on model quality, reliability, governance, and testing across the AI lifecycle.

Key Responsibilities

Data Science & AI Model Support

• Develop, analyze, and validate machine learning models used in AI-driven 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 (XAI).
• Design metrics and dashboards to monitor 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 and manage test datasets (synthetic, edge-case, adversarial).
• Support automated testing of ML pipelines and AI models.
• Assist with testing scenarios including:
• 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 scalable data pipelines using Databricks (Spark, Delta Lake).
• Write optimized SQL, PySpark, and Databricks notebooks for data processing and analysis.
• Collaborate with data engineers to ensure secure, reliable, and scalable data workflows.
• Implement best practices for data versioning, lineage, and reproducibility.

AI Quality, Governance & Security

• Support AI quality and readiness frameworks, including model validation and auditability.
• Assist with AI governance, documentation, and compliance requirements.
• Contribute to AI risk assessments, including model explainability and failure analysis.
• Collaborate 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 in a similar role.
• Strong hands-on experience with Databricks (Spark, Delta Lake, notebooks).
• Proficiency in Python, SQL, and PySpark.
• Experience with machine learning model training, evaluation, and 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, MLOps, or model validation frameworks.
• 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