The professional will work across data engineering, statistical and machine-learning modeling, and stakeholder engagement to take marketing-science projects from ambiguous business questions to trusted, actionable recommendations.
Senior Data Science Practice (7+ years): Demonstrated ownership of end-to-end data science, machine learning, advanced analytics, or quantitative modeling projects in production or client-facing environments. A quantitative degree is welcome, but equivalent professional experience is also valued.
Marketing Measurement Expertise (3+ years): Strong hands-on experience in at least one of the following: MMM, attribution modeling, incrementality testing, causal inference, econometrics, or related media and marketing effectiveness work. Media or advertising experience is highly valued, although equivalent measurement expertise in another domain may qualify.
Python and/or R (5+ years): Proficiency in building, validating, and maintaining statistical or machine-learning models, with the ability to explain modeling choices, assumptions, and limitations.
SQL and Data Preparation (5+ years): Strong SQL skills for data extraction, transformation, validation, and analysis, along with practical experience preparing reliable modeling datasets from diverse sources.
Statistical Modeling and Machine Learning (5+ years): Ability to select, evaluate, and explain methods such as regression, classification, clustering, forecasting, experiment analysis, and simulation in a business context.
Stakeholder Communication (5+ years): Ability to translate analytical results into decision-ready narratives, communicate uncertainty, and collaborate effectively with cross-functional and client-facing teams.
Project and Technical Leadership (3+ years): Experience driving delivery from discovery and design through implementation, validation, and presentation, while mentoring colleagues and improving team practices.
Skills and experience
Required skills are essential for this role. Nice-to-have skills add value but do not replace the requirements.
Senior Data-Science · 4 years minimum · RequiredMarketing Measurement Depth · 3 years minimum · RequiredPython and/or R · 4 years minimum · RequiredSQL · 4 years minimum · RequiredStatistical Modeling · 4 years minimum · RequiredMachine Learning · 4 years minimum · RequiredStakeholder Communication · 4 years minimum · RequiredProject and Technical Leadership · 3 years minimum · RequiredEnglish · Advanced · Required
Role details and responsibilities
- Build and improve marketing measurement solutions, including Marketing Mix Modeling (MMM), multi-touch attribution, incrementality testing, causal inference, budget optimization, and digital media analytics.
- Own the full data science lifecycle: clarify business objectives, source and prepare data, select and validate methodologies, communicate limitations, and present recommendations that inform media planning and investment.
- Design econometric, predictive, simulation, and scenario-planning models to quantify marketing impact, ROI, and trade-offs across channels and audiences.
- Ingest, clean, standardize, validate, and transform structured and unstructured data through scalable, reproducible pipelines while maintaining data quality and governance.
- Apply statistical and machine-learning techniques, including regression, classification, clustering, and time-series forecasting, based on business needs.
- Produce clear analyses, dashboards, and visualizations, translating complex findings into concise narratives for technical and non-technical stakeholders.
- Partner with Media Planning, Strategy, Digital Investment, and Client Services teams to define measurable questions, align assumptions, and deliver projects on schedule.
- Lead individual project workstreams, contribute to research and evaluation of new methods, and strengthen code quality, documentation, reproducibility, and modeling practices.
- Mentor junior data scientists and analysts through hands-on technical guidance and constructive feedback.