Data analysis and modeling:

• Extract insights only using necessary, proportionate data, ensuring privacy compliance throughout analysis and modelling.

Privacy-preserving techniques:

• Apply privacy-enhancing technologies like differential privacy, anonymization, aggregation and federated learning to protect data.

Privacy impact assessments:

• Conduct assessments during the planning and design phases to evaluate potential privacy impacts and identify necessary mitigations.

Govern data use and provenance:

• Process data for its intended purpose, manage its lifecycle and track consent and provenance to ensure ethical reuse.

Ensure fairness and protect sensitive data:

• Identify and address bias risks in AI models and safeguard against unintended inference of sensitive data.

Collaboration:

• Work closely with privacy engineers, legal and compliance teams to align data activities with privacy policies and standards.

Technical competencies:

• Proficiency in statistical analysis
• Machine learning
• Data anonymization
• Encryption
• Data lifecycle management

Areas of experience:

• Programming
• Data science
• Algorithm development
• Artificial intelligence
• Data engineering
• Cloud-based analytics

AI lifecycle experience:

Active across all stages:

• Planning
• Design
• Training
• Evaluation
• Implementation
• Deployment
• Online learning
• Post-deployment training and maintenance

Privacy tools:

• Familiarity with privacy-preserving technologies, such as federated learning, homomorphic encryption and synthetic data generation.

Privacy certifications:

• Certifications like the Certified Information Privacy Technologist or other data protection credentials to enhance privacy expertise.

Reports to:

• Chief data officer, head of AI or chief technology officer

Cross-functional collaboration:

To ensure privacy is maintained throughout the AI development process, the data scientist works with:

• Privacy engineers
• UX designers
• Legal teams
• Product managers

• AI product
• Business operations
• Product development
• Marketing teams

Privacy-preserving technologies:

• Pretty Good Privacy
• Privacy Preserving Machine Learning
• TensorFlow Privacy
• Diffprivlib
• Microsoft SEAL

Guidance and standards:

• ISO/TR 31700
• NIST Privacy Framework
• European Union Agency for Cybersecurity

Privacy certifications:

• Certified Information Privacy Technologist and other certifications to deepen privacy expertise.

Effective data minimization:

• Collect and only use necessary data to achieve project goals — for example, data required to train or run DataStage models.

Successful integration of privacy-preserving technologies:

• Effectively use techniques like differential privacy, federated learning, and secure multi-party computation to protect data.

Transparency and accountability:

• Ensure AI systems are explainable and their data usage is transparent to stakeholders and end-users.

Trust and compliance:

• Achieve high levels of user trust through transparent data practices and maintain a record free of privacy violations.

High data utility:

• Extract actionable insights from data without compromising privacy, ensuring that all analyses align with ethical standards and regulations.

Bias mitigation and fairness:

• Maintain fair and unbiased AI models and mechanisms that continuously monitor and correct and deviations.