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A Big Four firm is standing up a dedicated synthetic data service team, signaling it sees generating artificial datasets for testing, AI development, and privacy-safe data sharing as a core, productized client offering rather than a niche technique.
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CSI Analytics – Synthetic Data Technical Analyst
Job Details
Position Title : CSI Analytics – Synthetic Data Technical Analyst
Job Purpose
The Synthetic Data Technical Analyst (Senior Associate) will be responsible for supporting the delivery, implementation, validation, and operation of Synthetic Data solutions across the organization. Working as part of the Synthetic Data Service team, the role will focus on generating, testing, validating, and maintaining Synthetic Data assets that support analytics, AI development, testing, performance testing, integration testing, business innovation, and secure data sharing.
The successful candidate will possess strong technical capabilities across Python development, SQL, Data Engineering, Machine Learning, Statistical Analysis, and Data Modelling disciplines. The role requires a hands-on technical practitioner responsible for analysing source datasets, implementing generation logic, validating outputs, and supporting the development of reusable Synthetic Data generation frameworks.
Working under the guidance of the Synthetic Data Technical Lead, the analyst will contribute to the delivery of Synthetic Data engagements from requirements gathering through implementation, validation, documentation, and support.
Key Role and Responsibilities
This Synthetic Data Technical Analyst will be responsible for delivering scalable, secure, and high quality synthetic data solutions to meet exceptional client and internal stakeholder expectations. Standard work includes but is not limited to:
Synthetic Data Solution Delivery
Synthetic Data Engineering & Validation
Governance, Documentation & Compliance
Service Support & Continuous Improvement
The role may require periodic allocation of additional time to support critical deliveries, operational activities, or collaboration with global teams operating across multiple time zones.
Qualifications, Experience and Skills
Qualifications
Experience
The successful candidate should have experience in:
Data & Analytics Experience
Technical Skills/Competencies
Advanced Level
Working Level
Candidates must demonstrate practical Synthetic Data engineering experience using Python, SQL, statistical modelling, machine learning, business-rule implementation, data modelling, and validation techniques. Experience with commercial Synthetic Data platforms is advantageous but not mandatory.
Behavioral Competencies
Analytical thinking and problem solving
Additional Candidate Validation Requirement:
Synthetic Data Experience Validation (Mandatory)
Candidates must be able to demonstrate at least two enterprise-scale Synthetic Data implementations where they personally:
Candidates should be prepared to discuss:
Experience limited to vendor demonstrations, academic research, training courses, proof-of-concepts, or theoretical knowledge will not be considered sufficient.
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