Be at the heart of actionFly remote-controlled drones into enemy territory to gather vital information.

Apply Now

Data Scientist - Synthetic Data Team

Ipsos
1 year ago
Applications closed

Related Jobs

View all jobs

Data Scientist

Data Scientist

Data Scientist

Data Scientist

Data Scientist

Data Scientist - Outside IR35

Data Scientist - Synthetic Data Team 

Who We Are

Ipsos is one of the world’s largest research companies and currently the only one primarily managed by researchers, ranking as a #1 full-service research organization for four consecutive years. With over 75 different data-driven solutions, and presence in 90 markets, Ipsos brings together research, implementation, methodological, and subject-matter experts from around the world, combining thematic and technical experts to deliver top-quality research and insights. Simply speaking, we help the biggest companies solve some of their biggest problems, serving more than 5000 clients across the globe by providing research, data, and insights on their target markets.

Role Overview:

As a Data Scientist on the Synthetic Data Team at Ipsos, you will play a crucial role in advancing our capabilities in leveraging synthetic data for market research. You will collaborate with a talented team of data scientists and engineers to explore use cases, validate methodologies, and develop innovative solutions that harness the potential of synthetic data to enhance our research offerings.

Impact of Role:

Your work will have a significant impact on shaping the future of market research at Ipsos. By pioneering the use of synthetic data, you will contribute to the development of more efficient, cost-effective, and privacy-preserving research methods. Your insights and solutions will empower our clients to make data-driven decisions with greater confidence and agility.

What you will be doing:

Design, implement, and evaluate synthetic data generation algorithms and models, either proprietary, prebuilt or third-party, including Generative Adversarial Networks (GANs) and other generative AI techniques. Test and validate the effectiveness of synthetic data in replicating the statistical properties and patterns of real-world data while ensuring privacy standards are met. Collaborate with cross-functional teams to identify and prioritise use cases for synthetic data in areas such as market research, customer insights, and predictive analytics. Develop and test synthetic data use cases, exploring innovative applications and improving data quality and utility for various analytical purposes. Set up metrics and frameworks to assess the quality, diversity, and utility of synthetic data generated, ensuring it meets the required standards for different use cases Stay abreast of latest advancements and research in generative AI & synthetic data techniques. Conduct research and experiments to push the boundaries of Ipsos' synthetic data capabilities. Work closely with data engineers, analysts, and other stakeholders to integrate synthetic data solutions into projects, teams and Service Lines  Communicate complex technical concepts and findings effectively to less technical stakeholders, providing insights and recommendations based on synthetic data analyses. Document methodologies, processes, and findings to ensure transparency and reproducibility of synthetic data projects. Share knowledge and best practices within the team and across the organisation, contributing to training sessions and internal seminars.

You're the right person, if…

You have a PhD or a Master’s in a quantitative field such as Statistics, Mathematics, Computer Science, or a related discipline You have a solid foundation in statistical analysis, data mining, and machine learning, with experience applying these skills to real-world datasets. You are proficient in Python and have experience with data science libraries like pandas, scikit-learn, and TensorFlow or PyTorch You have a deep understanding of generative models, such as Generative Adversarial Networks (GANs), Variational Autoencoders (VAEs) and other techniques used for synthetic data generation such as SMOTE You are knowledgeable about the ethical considerations and best practices in generating synthetic data, including privacy preservation and data security. You possess strong problem-solving skills and can develop innovative solutions to complex data challenges, particularly in the context of synthetic data use cases You have a curious mindset, always looking to explore new methodologies, tools, and applications in the field You are familiar with data visualisation tools (., Matplotlib, Seaborn, or Tableau) and can effectively present data insights and findings You have experience with big data technologies and cloud platforms (., AWS, Google Cloud, Azure) and can work with large-scale datasets You have a good understanding of data privacy regulations and best practices, ensuring that synthetic data projects comply with legal and ethical standards You enjoy working in a collaborative environment, partnering with data engineers, analysts, and other team members to integrate and apply synthetic data solutions. You are an effective communicator who can clearly articulate complex concepts and findings to both technical and non-technical stakeholders

If you don’t meet 100% of the requirements, we encourage all who feel they might be a fit for the opportunity to apply. 


What’s in it for you:

At Ipsos you’ll experience opportunities for Career Development, an exceptional benefits package (including generous annual leave/paid time off, healthcare plans, wellness benefits), a flexible workplace policy, and a strong collaborative culture.

Subscribe to Future Tech Insights for the latest jobs & insights, direct to your inbox.

By subscribing, you agree to our privacy policy and terms of service.

Industry Insights

Discover insightful articles, industry insights, expert tips, and curated resources.

Machine Learning Recruitment Trends 2025 (UK): What Job Seekers Need To Know About Today’s Hiring Process

Summary: UK machine learning hiring has shifted from title‑led CV screens to capability‑driven assessments that emphasise shipped ML/LLM features, robust evaluation, observability, safety/governance, cost control and measurable business impact. This guide explains what’s changed, what to expect in interviews & how to prepare—especially for ML engineers, applied scientists, LLM application engineers, ML platform/MLOps engineers and AI product managers. Who this is for: ML engineers, applied ML/LLM engineers, LLM/retrieval engineers, ML platform/MLOps/SRE, data scientists transitioning to production ML, AI product managers & tech‑lead candidates targeting roles in the UK.

Why Machine Learning Careers in the UK Are Becoming More Multidisciplinary

Machine learning (ML) has moved from research labs into mainstream UK businesses. From healthcare diagnostics to fraud detection, autonomous vehicles to recommendation engines, ML underpins critical services and consumer experiences. But the skillset required of today’s machine learning professionals is no longer purely technical. Employers increasingly seek multidisciplinary expertise: not only coding, algorithms & statistics, but also knowledge of law, ethics, psychology, linguistics & design. This article explores why UK machine learning careers are becoming more multidisciplinary, how these fields intersect with ML roles, and what both job-seekers & employers need to understand to succeed in a rapidly changing landscape.

Machine Learning Team Structures Explained: Who Does What in a Modern Machine Learning Department

Machine learning is now central to many advanced data-driven products and services across the UK. Whether you work in finance, healthcare, retail, autonomous vehicles, recommendation systems, robotics, or consumer applications, there’s a need for dedicated machine learning teams that can deliver models into production, maintain them, keep them secure, efficient, fair, and aligned with business objectives. If you’re hiring for or applying to ML roles via MachineLearningJobs.co.uk, this article will help you understand what roles are typically present in a mature machine learning department, how they collaborate through project lifecycles, what skills and qualifications UK employers look for, what the career paths and salaries are, current trends and challenges, and how to build an effective ML team.