Senior Econometrician

b Overview /b p 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. /p b Pay / Benefits /b ul li hybrid working in London /li li career development opportunities /li li confidential discussion process /li li referral rewards scheme /li li competitive salary DoE /li /ul b Responsibilities /b ul li Manage full modelling lifecycle from requirements to delivery /li li Prepare and clean data for analysis /li li Apply econometric methods (regression, adstock, lag structures, saturation, interactions) to assess marketing performance /li li Validate models and assess performance using diagnostic tests /li li Create clear presentations explaining findings, implications, and recommendations /li li Translate statistical outputs into actionable business insights /li li Lead projects with limited supervision and drive delivery /li li Identify opportunities to improve analytic processes and deliverables /li li Contribute to development of new analytical approaches and offerings /li li Collaborate with colleagues and clients to ensure outputs address business questions /li li Share knowledge and mentor less experienced team members /li /ul b Key requirements /b ul li 3+ years’ MMM experience /li li Strong econometric modelling with R, Python, EViews or SAS /li li Experience cleaning and transforming large datasets /li li Understanding of linear and log-linear modelling approaches /li li Knowledge of regression, adstock, lag structures, saturation and interactions /li li Model validation and diagnostic techniques /li li Familiarity with Python libraries (Pandas); Parquet knowledge is a plus /li li Exposure to machine learning (Random Forest) is beneficial /li li Ability to manage modelling assignments independently /li li Experience presenting analyses in reports and presentations /li li Clear communication for non-technical audiences /li li Client-facing experience and stakeholder engagement /li li Interest in mentoring juniors and developing team capabilities /li /ul ul li problem-solving /li li clear communication /li li stakeholder engagement /li li R /li li Python /li li EViews /li /ul