Product Data Science Lead
Octopus Energy
Lead Research Data Scientist – Fraud Detection, Market Research
Kantar
full-time
Posted on:
Location Type: Hybrid
Location: London • 🇬🇧 United Kingdom
Visit company websiteJob Level
Senior
Tech Stack
AWSAzureCloudKafkaPythonPyTorchScikit-LearnSQLTensorflow
About the role
- Spearhead research-driven initiatives in detecting and preventing fraud and poor data quality within online market research surveys
- Develop and validate advanced models and systems for fraud detection
- 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
Requirements
- 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
Benefits
- 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
Applicant Tracking System Keywords
Tip: use these terms in your resume and cover letter to boost ATS matches.
Hard skills
fraud detectiondata quality solutionsmachine learninganomaly detectionbehavioural modellingtime-series analysisNLP techniquesDeep LearningLLMs evaluationPython
Soft skills
thought leadershipmentorshipcommunicationcollaborationbest practices promotion
Certifications
PhD in Data ScienceMSc in relevant field
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