Senior Econometrician

Overview

In this Senior Econometrician role, you will lead end-to-end Marketing Mix Modelling projects for consumer brands, using advanced econometrics to measure and optimise marketing effectiveness. You’ll work within a technically focused team, guiding project delivery from data prep to insights and client presentations. The role combines hands-on modelling with interpretation, stakeholder engagement, and junior coaching to scale analytical capabilities. You’ll influence commercial decisions by translating complex outputs into practical recommendations and exploring new analytical approaches. This is a hybrid London position with autonomy to drive impact and contribute to growth.

Pay / Benefits

• hybrid working in London
• career development opportunities
• confidential discussion process
• referral rewards scheme
• competitive salary DoE

Responsibilities

• Manage full modelling lifecycle from requirements to delivery
• Prepare and clean data for analysis
• Apply econometric methods (regression, adstock, lag structures, saturation, interactions) to assess marketing performance
• Validate models and assess performance using diagnostic tests
• Create clear presentations explaining findings, implications, and recommendations
• Translate statistical outputs into actionable business insights
• Lead projects with limited supervision and drive delivery
• Identify opportunities to improve analytic processes and deliverables
• Contribute to development of new analytical approaches and offerings
• Collaborate with colleagues and clients to ensure outputs address business questions
• Share knowledge and mentor less experienced team members

Key requirements

• 3+ years’ MMM experience
• Strong econometric modelling with R, Python, EViews or SAS
• Experience cleaning and transforming large datasets
• Understanding of linear and log-linear modelling approaches
• Knowledge of regression, adstock, lag structures, saturation and interactions
• Model validation and diagnostic techniques
• Familiarity with Python libraries (Pandas); Parquet knowledge is a plus
• Exposure to machine learning (Random Forest) is beneficial
• Ability to manage modelling assignments independently
• Experience presenting analyses in reports and presentations
• Clear communication for non-technical audiences
• Client-facing experience and stakeholder engagement
• Interest in mentoring juniors and developing team capabilities

• problem-solving
• clear communication
• stakeholder engagement
• R
• Python
• EViews

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