Overview
I’m working with a global digital consultancy that urgently needs a Data Science Lead to drive advanced statistical modelling, experimentation strategies, and causal analysis across major brand portfolios. This is a senior, hands-on role working in a fast-paced, highly collaborative environment.
What you’ll be doing
* Design, implement, and analyse causal inference experiments, including natural experiments and quasi-experimental methods
* Develop and apply conformal prediction frameworks to provide reliable uncertainty estimates for machine learning models
* Identify and control for confounding variables in observational studies
* Create robust statistical methodologies for causal effect estimation
* Collaborate with cross-functional teams to translate business questions into rigorous experimental designs
* Present technical findings to stakeholders in clear, actionable terms
What we’re looking for
* Advanced degree (MS or PhD) in a quantitative discipline with deep understanding of statistics
* 3+ years of professional experience applying statistical methods to real-world data
* Demonstrated expertise in experimental design, including randomized controlled trials and observational study methodologies
* Strong understanding of conformal prediction theory and applications
* Proficiency in programming languages such as Python or R, and relevant statistical packages
* Experience with causal inference frameworks (e.g., potential outcomes, causal graphs, do-calculus)
* Knowledge of modern machine learning techniques and how they intersect with causal reasoning
* Excellent communication skills, with the ability to explain complex statistical concepts to non-technical audiences
* Agency or client-service experience is highly desirable — familiarity with fast-moving campaigns and cross-functional collaboration is a big plus
Preferred skills
* Experience with heterogeneous treatment effect estimation
* Familiarity with Bayesian methods for causal inference
* Background in epidemiology is a plus
* Experience working with a causal inference ecosystem (e.g., PyWhy, CausalImpact, Synth, GeoLift)
Contract length: 2 months initially
Working pattern: Hybrid / remote (approx. 2–3 days onsite in London)
If this sounds like you, drop me a DM or email your CV to zoe.hinkinson@propellondon.com
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