Data Scientist – Consumer Behaviour

Overview

In this role you work with large-scale behavioural data to derive actionable insights for leading brands. You will define cohorts, apply statistical methods and causal inference techniques, and contribute to data products and client reporting. You’ll collaborate with a small, high-performing Data Science team and engage with cross-functional stakeholders to turn complex data into clear recommendations. This position offers exposure to exciting projects and real client challenges within a world-leading consumer behaviour data environment.

Pay / Benefits

• hybrid working (3 days in office per week)
• salary 45,000 – 60,000 depending on skills & experience
• referral scheme with gift/voucher rewards
• opportunity to work with leading brands
• small, high-performing team
• exposure to data products and client insights

Responsibilities

• Work with large-scale behavioural datasets across multiple platforms
• Define cohorts and datasets using SQL, including complex aggregations, CTEs and window functions
• Apply statistical methods including hypothesis testing, confidence intervals and experimental analysis
• Work with causal inference techniques such as A/B testing, difference-in-differences and matching
• Investigate selection bias, confounding and other factors affecting result validity
• Develop and maintain data aggregation pipelines and datasets used across the business
• Contribute to PII detection and client reporting processes
• Validate analytical results against source data and investigate unexpected findings
• Write clear, maintainable Python code that can be understood and reused by others
• Work collaboratively through GitHub, pull requests and code reviews
• Present findings, methodologies and caveats to clients and internal stakeholders
• Use AI and coding assistants effectively while reviewing and validating their output
• Contribute to the development of analytical and data products as the team evolves

Key requirements

• 1-3 years’ Data Science experience, or equivalent demonstrated experience
• Strong Python skills, particularly pandas and NumPy
• Strong SQL skills, including CTEs, window functions and aggregation
• Solid understanding of applied statistics, including hypothesis testing and confidence intervals
• MMM (Marketing Mix Modelling) experience or related exposure
• Understanding of selection bias, confounding and statistical method assumptions
• Knowledge of causal inference techniques such as A/B testing, difference-in-differences or matching
• Habit of validating results against underlying data
• Strong written and verbal communication skills
• Curious and sceptical mindset with investigative instincts
• Experience using AI or coding assistants with validation discipline

• clear written and verbal communication
• curiosity and scepticism
• problem-solving mindset
• Python (pandas, NumPy)
• SQL (CTEs, window functions, aggregations)
• Applied statistics (hypothesis testing, confidence intervals)

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