Director of Data Governance - Responsible AI

Overview:

We are seeking a Director of Data Governance Responsible AI to lead our efforts at the forefront of Responsible AI (RAI) and AI Ethics at G42. This role offers an opportunity to develop and implement best practices in relation to RAI solutions (across, governance, technical audits and policy) which directly impact industry practices. This role requires a data governance expert who will be responsible for ensuring that G42’s data ecosystem is clean, ethical, unbiased, and aligned with global regulatory and compliance standards. The ideal candidate will drive the Group’s strategy and collaborate closely with internal teams and external partners to create impactful and ethical AI technologies.
This role sits at the intersection of data engineering, AI ethics, and governance, requiring deep technical expertise in data pipelines, bias detection, privacy-enhancing technologies, and compliance frameworks. You will work closely with data scientists, AI engineers, legal teams, and policymakers to ensure that G42’s AI models are trained on high-quality, diverse, and representative datasets while minimizing risks associated with bias, misinformation, and unethical data usage.
You will lead the development of responsible data practices, AI fairness protocols, and scalable data governance frameworks, ensuring that G42’s AI-driven products remain trustworthy, compliant, and impactful.
 

Responsibilities:

Data Quality, Integrity & Bias Mitigation

• Develop and implement data governance strategies to ensure AI models are trained on clean, representative, and high-integrity datasets.
• Design and oversee bias detection and mitigation frameworks, ensuring fairness across AI models used in critical sectors.
• Implement data lineage tracking, provenance validation, and anomaly detection to ensure transparency and accountability in AI training data.
• Collaborate with AI/ML teams to establish fairness-aware pre-processing techniques, mitigating data-driven disparities before they propagate into models.
• Develop synthetic data generation strategies where appropriate, ensuring diverse and privacy-preserving datasets for AI applications.

Data Governance & Compliance

• Establish data governance policies aligned with global AI and data protection regulations (e.g., GDPR, UAE Data Protection Law, AI Act, ISO 42001).
• Implement data access control mechanisms, ensuring compliance with privacy laws, AI risk management standards, and regulatory requirements.
• Work with security and legal teams to integrate privacy-enhancing technologies (PETs) such as differential privacy, federated learning, and homomorphic encryption into AI pipelines.
• Conduct AI data audits to ensure models are not trained on misleading, discriminatory, or unauthorized datasets.
• Develop automated compliance monitoring tools, enabling continuous governance of data used across G42’s AI systems.

Ethical AI & Responsible Data Strategies

• Lead initiatives to define ethical AI data collection, annotation, and labeling practices, ensuring transparency and accountability.
• Design diversity-aware data augmentation techniques, ensuring models perform equitably across demographics, geographies, and use cases.
• Develop and oversee algorithmic auditing processes, identifying biases in model outputs and addressing disparities at the data level.
• Establish an AI Explainability & Interpretability Framework, ensuring datasets contribute to explainable and trustworthy AI models.
• Collaborate with external AI ethics groups, regulators, and civil society organizations
• to refine best practices in AI data governance.
•  

Technical Leadership & AI Data Infrastructure Optimization

• Lead the development of data pipeline monitoring tools, ensuring real-time detection of drift, bias, and quality degradation in AI training data.
• Oversee data pipeline automation, implementing ETL best practices, scalable data wrangling techniques, and real-time data validation checks.
• Work with data engineering teams to ensure AI training datasets remain secure, scalable, and compliant with enterprise data governance policies.
• Guide AI/ML teams in feature engineering best practices, ensuring bias-aware, interpretable, and robust feature representations.
• Advocate for open data and standardized benchmarking practices, fostering transparency and collaboration in AI research and development.

Cross-Functional Collaboration & Thought Leadership

• Partner with AI engineers, data scientists, and compliance officers to embed
• responsible data practices into the AI development lifecycle.

Qualifications:

Required

• Master’s or PhD in Data Science, Computer Science, AI, Statistics, or a related field.
• 8+ years of experience in data governance, AI data ethics, machine learning, or related fields.
• Deep expertise in data governance frameworks, privacy regulations, and AI risk management standards.
• Strong proficiency in data engineering tools (e.g., Apache Spark, Databricks, Airflow, SQL, NoSQL).
• Experience with AI fairness and bias detection frameworks (e.g., IBM AI Fairness 360, Aequitas, SHAP, LIME, Explainable AI).
• Familiarity with privacy-enhancing technologies (e.g., federated learning, differential privacy, homomorphic encryption).
• Strong understanding of machine learning workflows, including feature engineering, data augmentation, and model interpretability.
• Experience working with large-scale datasets, real-time streaming data, and unstructured data pipelines.
• Ability to drive cross-functional collaboration between data engineers, AI researchers, and legal/compliance teams.

Preferred

• Previous experience working in high-stakes AI industries (e.g., healthcare, finance,
• defense, government).
• Experience designing real-time data validation pipelines and automated AI bias monitoring tools.
• Contributions to open-source AI fairness, privacy, or governance projects.
• Technical leadership experience, with a proven track record of guiding data strategy for AI-driven enterprises.

Personal Attributes

• Passionate about the ethical implications of AI and emerging technologies
• Adaptable and thrives in a fast-paced, evolving environment
• Committed to promoting responsible innovation in the tech industry
• Excellent judgment and discretion when handling sensitive information