Join EY as a Synthetic Data Technical Lead and play a pivotal role in establishing and scaling our Synthetic Data Service capability. This leadership position demands a blend of deep synthetic data expertise, strong leadership qualities, and exceptional stakeholder management skills. You will be the technical authority and service owner, driving the successful adoption of synthetic data solutions while upholding stringent standards for privacy, security, governance, and data quality.

This is a hands-on role where you will lead a team and collaborate closely with Data, AI, Engineering, Security, Privacy, Risk, and Business stakeholders. Your work will enable innovative synthetic data solutions for testing, analytics, AI development, data sharing, and business innovation.

Initially, the focus will be on building and operationalizing synthetic data generation using reusable blueprints, Python, SQL, machine learning, statistical modeling, and Azure-based engineering. The role will evolve towards reusable frameworks, API-driven generation, and AI-assisted self-service capabilities.

Lead the definition and execution of the Synthetic Data Service strategy, roadmap, and operating model. Establish robust service offerings, delivery methodologies, and engagement frameworks. Drive enterprise adoption by identifying opportunities, demonstrating value, and educating stakeholders on synthetic data benefits. Serve as the primary technical authority for synthetic data generation methodologies, data modeling, machine learning approaches, and engineering standards. Oversee the design and delivery of synthetic data solutions using Python, SQL, statistical modeling, and machine learning techniques. Establish and maintain governance processes ensuring compliance with legal, regulatory, and security requirements. Own the operational performance, continuous improvement, and demand management of the synthetic data service. Lead, mentor, and develop a team of Synthetic Data Technical Analysts, fostering a culture of excellence and innovation.

A Bachelor’s degree in Computer Science, Data Science, Engineering, Information Systems, Mathematics, Statistics, or a related field is required. A Master's degree or relevant specialist certifications are considered advantageous.

Possess a minimum of 7 years of practical experience in designing, building, validating, and governing synthetic data solutions within enterprise environments. This includes hands-on experience with Python, SQL, data modeling, statistical modeling, data profiling, synthetic data generation methodologies, machine learning, and data privacy/governance.

Demonstrate practical experience in at least two enterprise-scale synthetic data implementations where you personally designed, built, and validated generation solutions using Python, SQL, and machine learning. You must have experience defining business rules, schemas, relationships, and distributions, and establishing validation controls.

Key technical competencies include Python, SQL, Synthetic Data Generation Techniques, Data Modelling, Statistical Modelling, Data Quality & Validation, Data Privacy & Governance, and Technical Architecture. Advanced skills in Machine Learning, Azure Data Services, Data Engineering, and API Integration are also crucial.