About the role

Lead Research Data Scientist combating fraud and enhancing data quality in online market research. Spearheading research-driven initiatives using machine learning and behavioral analytics for Kantar.

Responsibilities

Spearhead our research-driven initiatives in detecting and preventing fraud and poor data quality within online market research surveys
Develop and validate advanced models and systems that uphold the integrity of our data and the robustness of our insights
Design, validate, and deploy novel machine learning models to detect fraudulent survey responses, including bots, duplicate entries, low-quality survey data and improbable or non-relevant responses
Develop and maintain real-time scoring systems in production to assess respondent authenticity and engagement
Conduct in-depth analysis of behavioural patterns, metadata, and response timing to uncover anomalies and suspicious activity
Collaborate with survey operations, panel management, and research teams to embed fraud detection tools into survey workflows
Patent novel algorithms & approaches to fraud detection within Market Research
Provide thought leadership and mentorship to the team; promote best practices in ethical data handling, reproducible research, and experimental design
Contribute to the wider Kantar data science ecosystem by presenting methodologies, publishing internal white papers, and facilitating knowledge exchange on fraud detection
Stay abreast of academic and industry developments in fraud tactics, data validation, and respondent quality assurance

PhD in Data Science (or a highly relevant MSc plus real-world research experience), Statistics, Mathematical Modelling, Computer Science, or a related quantitative field
Seniority and proven experience in data science, with at least 2 years focused on fraud detection, survey analytics, automated data quality solutions, or related research applications
Strong proficiency in Python, R, SQL, and machine learning libraries (e.g., scikit-learn, XGBoost, TensorFlow, pyTorch)
Experience with Kafka, ML Ops, and CI/CD pipelines is advantageous
Experience with ML Ops & cloud platforms such as Azure ML and AWS is required
Deep understanding of anomaly detection, behavioural modelling, and time-series analysis
Deep research experience with NLP techniques for validating open-ended responses including applications Deep Learning for Gen AI / LLMs
Experience with evaluation of LLMs such as LLM as a judge & human evaluated testing.
Strong communication skills, with the ability to translate technical findings into research insights and actionable recommendations

Competitive salary and performance-based bonuses
Flexible working hours and hybrid work model
Access to rich global survey datasets and cutting-edge tools, including state-of-the-art fraud detection models
A collaborative, mission-driven team focused on data integrity, reproducibility, and innovation
Opportunities for professional development, academic collaboration, and leadership growth
Support for presenting at academic and industry conferences, and contributing to peer-reviewed publications where appropriate