Head of Platform & Engineering, Synthetic Data

The Synthetic Data Research team is building Ipsos’ next-generation platform for synthetic data and generative AI, turning cutting-edge methods into practical tools that can be used safely and confidently across the business. We focus on two core products:

• Data Augmentation Workbench: a self-serve internal platform that enables teams to train models and generate synthetic data through secure APIs and streamlined workflows, with evaluation and governance built in from day one.
• Digital Twins: agentic, respondent-grounded LLM “synthetic panellists” designed to simulate behaviours and survey responses, supported by rigorous validation, privacy safeguards, and strong auditability.

Our work sits at the intersection of software engineering, machine learning, privacy, and market research methodology. We collaborate with leading academic institutions (including Stanford University) to ensure our approach is scientifically robust while remaining focused on real-world impact. Ultimately, our goal is to deliver data collection efficiencies, new product innovation, and defensible scientific frameworks that can scale to thousands of colleagues and clients. We’re a cross-disciplinary group, bringing together market researchers, mathematicians, computer scientists, data scientists, and data engineers, to build capabilities that shape how insights are created in the future.

How you’ll make an impact: You’ll lead the engineering function of the Synthetic Data Team - setting technical direction, building a high-performing team, and delivering a secure, reliable, compliant platform used by internal stakeholders globally. You’ll raise the bar on engineering excellence in the whole of Ipsos, setting the standard across the business, while partnering closely with data science, research methodologists, product, security/privacy, and legal.

In practice, you will:

• Build a platform people trust. Establish reliability, security, governance, and auditability so synthetic outputs can be used with confidence in business-critical contexts.
• Standardise how ML work ships. Create repeatable patterns for data → training → evaluation → release using Vertex AI Custom Jobs, Experiments, and Pipelines, so we can move fast without losing reproducibility.
• Enable self-serve at scale. Make it easy for internal teams to generate synthetic data and run experiments through stable APIs, clear documentation, and strong operational support.
• Operationalise privacy-by-design. Embed controls and guardrails that reflect respondent-data sensitivity and MR methodology requirements.
• Create a healthy delivery machine. Mature the SDLC, sprint execution, and engineering standards across a mixed team of backend, data, MLOps, and infra.

Infrastructure & Cloud: Google Cloud Platform (GCP), Kubernetes (GKE), Docker, Infrastructure as Code (Terraform/Pulumi).

MLOps & Pipeline Platforms: Kubeflow Pipelines (KFP), Vertex AI, Model Registries, and automated ML CI/CD systems.

Backend & Data Services: Python, FastAPI, Asynchronous task brokers (Celery/RabbitMQ), PostgreSQL, Vector Databases.

Data Architecture: High-throughput columnar stores (BigQuery, Parquet) and distributed processing frameworks.

AI/ML Foundations: Ecosystems supporting PyTorch, Large Language Models (LLMs), and Deep Generative Tabular Models.

What you’ll do:

• Define Technical Vision & Strategy: Own the high-level architecture and platform strategy. You will design the roadmap for scaling our infrastructure to support heavy, asynchronous GenAI workloads (both LLM digital twins and deep tabular augmentation).
• Lead & Scale the Team: Build, mentor, and manage a high-performing team of Software Engineers, Data Engineers, and MLOps Engineers. You will foster a culture of engineering excellence, continuous delivery, and operational rigour.
• Bridge Research & Engineering: Act as the critical translator between our Applied ML Scientists and the Engineering team. You will ensure that cutting-edge algorithmic research is transitioned smoothly into stable, cost-effective, and scalable production systems.
• Ensure Enterprise Readiness: Drive initiatives around system security, data privacy (e.g., handling PII in LLMs and synthetic data), compliance, and high availability (SLAs).
• Optimize Cloud Economics: Take ownership of cloud infrastructure costs. You will guide the architectural decisions that balance raw GPU compute power with cost-efficient resource allocation, dynamic scaling, and caching.
• Platform Evangelism: Standardize our internal developer platform, creating the abstractions and paved roads that allow ML scientists to deploy new models rapidly without worrying about underlying infrastructure.

What you’ll need:

• Technical Leadership
• Engineering Management: Proven track record of managing, scaling, and mentoring cross-functional engineering teams (Software, Data, and DevOps/MLOps). Experience operating at a team-lead level in a fast-paced environment.
• Strategic Architecture: Deep experience designing highly available, distributed, and asynchronous systems at scale. You can architect solutions that process massive datasets and handle heavy, long-running compute tasks.
• Cloud & Infrastructure: Strong background in cloud-native ecosystems (GCP preferred) and container orchestration (Kubernetes). You know how to build platforms that scale globally and efficiently.
• GenAI & MLOps Domain Expertise
• ML Platform Experience: Experience leading teams that build or manage machine learning infrastructure, feature stores, and model deployment pipelines. You understand the unique pain points of MLOps compared to traditional DevOps.
• GenAI Context: Familiarity with the operational requirements of deploying modern AI systems—whether that's provisioning GPUs for LLM inference, managing vector databases, or orchestrating heavy PyTorch training runs in production.
• Business & Operational Acumen
• Cross-Functional Collaboration: Exceptional communication skills with a proven ability to align technical roadmaps with business objectives, working closely with Product, Research, and Executive teams.
• Enterprise Security & Compliance: Experience navigating enterprise-grade security requirements, SOC2 compliance, and implementing robust data governance frameworks.

We offer a comprehensive benefits package designed to support you as an individual. Our standard benefits include 25 days annual leave, pension contribution, income protection and life assurance. In addition, there are a range of health & wellbeing, financial benefits and professional development opportunities.

We have a hybrid approach to work and ask people to be in the office or with clients for 3 days per week. We appreciate you may have commitments outside of work.

We are committed to equality, treating people fairly, promoting a positive and inclusive working environment and ensuring we have diversity of people and views. We recognise that this is important for our business success - a more diverse workforce will enable us to better reflect and understand the world we research and ultimately deliver better research and insight to our clients. We are proud to be a member of the Disability Confident scheme, certified as a Level 2 Disability Confident Employer. We provide an inclusive and accessible recruitment process.