{"id":2750010,"url":"https://alion.io/job/ey-csi-analytics-synthetic-data-technical-lead","title":"CSI Analytics - Synthetic Data Technical Lead","company":{"id":2859,"name":"EY","domain":"ey.com","url":"https://alion.io/company/ey-com","size_band":"5000+","is_staffing_agency":false,"employer_type":"direct","is_intermediary":false,"listed_via":null,"ats_vendor":"SuccessFactors","truth_index":null},"role":"Backend","role_family":"Backend","seniority":"lead","employment_type":null,"work_mode":"on_site","remote_scope":null,"remote_scope_basis":null,"remote_working_hours":null,"hiring_geo_confidence":"structured","locations":["Bengaluru, India"],"countries":["IN"],"hiring_countries":[],"hiring_countries_total":0,"salary":null,"salary_estimate":{"min_usd":28000,"max_usd":64000,"period":"year","method":"role_seniority_country_remote_cell","sample_n":57},"experience_years_min":7,"visa_sponsorship":false,"relocation_package":false,"has_equity":false,"technologies":[{"name":"Azure","optional":false},{"name":"Machine Learning","optional":false},{"name":"Python","optional":false},{"name":"SQL","optional":false},{"name":"Synthetic Data","optional":false}],"status":"live","first_seen_at":"2026-10-16T02:00:00Z","employer_posted_date":"2026-10-16","last_verified_at":"2026-10-17T08:48:19Z","board_verified":true,"closed_at":null,"days_open":1,"trust":{"level":"ok","repost_count":null,"flags":[],"days_open":1},"description":"At EY, you’ll have the chance to build a career as unique as you are, with the global scale, support, inclusive culture and technology to become the best version of you. And we’re counting on your unique voice and perspective to help EY become even better, too. Join us and build an exceptional experience for yourself, and a better working world for all.\nCSI Analytics - Synthetic Data Technical Lead\nJob Details\nPosition Title: CSI Analytics - Synthetic Data Lead\nJob Purpose\nThe Synthetic Data Technical Lead (Assistant Director) will be responsible for establishing, leading, and scaling the Synthetic Data Service capability across the organization. This role will act as the technical authority, service owner, and people leader for Synthetic Data initiatives, ensuring the successful adoption of synthetic data solutions whilst maintaining the highest standards of privacy, security, governance, and data quality.\nThe successful candidate will combine deep synthetic data expertise with strong leadership and stakeholder management skills. They will be expected to define strategy, operating models, governance frameworks, and delivery standards whilst remaining actively involved in the design, implementation, validation, and continuous improvement of synthetic data solutions.\nThis is a highly hands-on leadership role. The successful candidate will lead a team of Synthetic Data Technical Analysts whilst working directly with Data, AI, Engineering, Security, Privacy, Risk, and Business stakeholders to deliver innovative synthetic data solutions that enable testing, analytics, AI development, data sharing, performance testing, integration testing, and business innovation.\nThe role will initially focus on building and operationalizing Synthetic Data generation capabilities using reusable blueprints, Python, SQL, Machine Learning, statistical modelling techniques, and Azure-based engineering patterns. Over time, the role will help evolve the capability toward reusable frameworks, API-driven generation services, and AI-assisted self-service synthetic data generation.\nKey Role and Responsibilities\nThis Synthetic Data Technical Lead will be responsible for delivering scalable, secure, and high quality synthetics data solutions to meet exceptional client and internal stakeholder expectations. Standard work includes but is not limited to:\nSynthetic Data Strategy & Service Leadership\nDefine and execute the Synthetic Data Service strategy, roadmap, operating model, governance framework, and adoption approach.\nEstablish service offerings, delivery methodologies, engagement models, support processes, and success measures.\nDrive enterprise adoption of Synthetic Data by identifying opportunities, demonstrating value, and educating stakeholders on appropriate use cases and benefits.\nAct as the primary point of contact and trusted advisor for Synthetic Data initiatives across business and technology teams.\nContinuously evaluate emerging market capabilities, industry trends, and new technologies to enhance the Synthetic Data service.\nTechnical Leadership & Solution Delivery\nAct as the technical authority for Synthetic Data generation methodologies, data modelling, machine learning approaches, and engineering standards.\nLead the design and delivery of Synthetic Data solutions using Python, SQL, statistical modelling, rule-based generation, simulation techniques, supervised and unsupervised machine learning models.\nDefine reusable generation blueprints, business rule frameworks, schema patterns, validation controls, and automation standards.\nLead the modelling of facts, dimensions, relationships, distributions, hierarchies, and business logic required to generate realistic Synthetic Data.\nReview and approve generation logic, Python code, validation methodologies, machine learning approaches, and delivery artefacts.\nDrive the evolution of the capability from manually engineered datasets toward reusable frameworks, Azure-based automation, API enablement, and future AI-assisted generation services.\nGovernance, Privacy & Risk Management\nEstablish and maintain governance processes that ensure Synthetic Data solutions meet organizational, legal, regulatory, and security requirements.\nDefine standards and controls covering privacy protection, disclosure risk, data quality, model governance, auditability, and responsible AI practices.\nLead the assessment and validation of Synthetic Data outputs to ensure appropriate levels of data utility, statistical fidelity, business realism, and privacy protection.\nWork closely with Data Governance, Information Security, Risk, Legal, and Privacy teams to obtain approvals and ensure compliance.\nEnsure all Synthetic Data solutions are fully documented, traceable, auditable, and aligned to enterprise governance standards.\nService Operations & Platform Evolution\nOwn the operational performance and continuous improvement of the Synthetic Data service.\nEstablish service metrics, KPIs, reporting, support processes, and operational controls.\nManage demand intake, prioritization, resource planning, and delivery governance across multiple engagements.\nMonitor service adoption, customer satisfaction, delivery quality, and business outcomes.\nSupport the evolution of the capability from a managed service model toward broader platform ownership, automation, and self-service capabilities.\nPeople Leadership & Capability Development\nLead, mentor, and develop a team of Synthetic Data Technical Analysts.\nFoster a culture of technical excellence, innovation, collaboration, and continuous learning.\nSupport recruitment, performance management, career development, and capability building within the team.\nProvide coaching on Synthetic Data methodologies, technical delivery, stakeholder engagement, and governance practices.\nPromote knowledge sharing and establish a community of practice for Synthetic Data across the organization.\nThe role will also require the periodic allocation of additional time on the job to support multiple demands, and escalating issues or to accommodate teams or staff in other time zones.\nQualifications, Experience and Skills\nQualifications\nBachelor’s degree in Computer Science, Data Science, Engineering, Information Systems, Mathematics, Statistics, or a related discipline.\nMaster's degree or relevant specialist certifications are advantageous.\nExperience\nSynthetic Data Engineering Expertise (Mandatory) - Minimum 7+ Years Practical Experience\nCandidates must demonstrate significant practical experience designing, building, validating, and governing Synthetic Data solutions within enterprise environments.\nThe successful candidate should have experience in:\nPython and SQL development.\nData modelling, schema design, and relationship modelling.\nStatistical modelling and distribution analysis.\nData profiling and data quality assessment.\nSynthetic Data generation methodologies.\nBusiness rule engineering and rule-based generation.\nMachine Learning and AI concepts.\nSynthetic Data validation and testing.Data privacy, governance, and disclosure risk management.\nAzure-based data engineering and automation solutions.\nTechnical Skills/Competencies\nExpert Level\nPython\nSQL\nSynthetic Data Generation Techniques\nData Modelling\nStatistical Modelling\nData Quality & Validation\nData Privacy & Governance\nTechnical Architecture\nStakeholder Management \nAdvanced Level\nMachine Learning\nAzure Data Services\nData Engineering\nAPI Integration\nDevOps & Automation\nMetadata Management\nData Product Management\nCandidates 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.\nBehavioral Competencies\nTeamwork\nIntegrity\nEmpathy & care\nOwnership\nAccountability \nAnalytical thinking and problem solving\nCoherent communication\nTime management and organization\nExtreme attention to detail\nAdditional Candidate Validation Requirement:\nSynthetic Data Experience Validation (Mandatory)\nCandidates must be able to demonstrate at least two enterprise-scale Synthetic Data implementations where they personally:\nDesigned and built Synthetic Data generation solutions.\nDeveloped Python, SQL, and Machine Learning-based generation logic.\nDefined business rules, schemas, relationships, and distributions.\nEstablished validation approaches and quality controls.\nWorked directly with Data Governance, Privacy, Security, Risk, and Business stakeholders.\nDelivered Synthetic Data solutions into operational or production use cases.\nCandidates should be prepared to discuss:\nSynthetic Data generation methodologies used.\nPython and Machine Learning approaches applied.\nBusiness rule implementation techniques.\nValidation and quality assessment approaches.\nData modelling decisions.\nKey challenges encountered and lessons learned.\nOutcomes achieved and business value delivered.\nExperience limited to vendor demonstrations, academic research, training courses, proof-of-concepts, or theoretical knowledge will not be considered sufficient.\nEY | Building a better working world\nEY exists to build a better working world, helping to create long-term value for clients, people and society and build trust in the capital markets.\nEnabled by data and technology, diverse EY teams in over 150 countries provide trust through assurance and help clients grow, transform and operate.\nWorking across assurance, consulting, law, strategy, tax and transactions, EY teams ask better questions to find new answers for the complex issues facing our world 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