Data Analytics Consultant

Omnicom Media is the world’s largest and most connected media and advertising organization. Bringing together leading agencies such as OMD, UM, PHD, Initiative, , Flywheel, TRKKN and YUNE, we combine media, data, technology, creativity and strategy in one integrated organization, helping brands grow in a rapidly changing media landscape. We’re proud to work with iconic brands like Nike, L’Oréal and McDonald’s as well as leading local and regional brands

About the role

As a Senior Data Science & Analytics Consultant, you help advertisers make better marketing decisions by combining advanced modelling, analytical rigour, and client consultancy. You combine a strong data science foundation with excellent consulting and communication skills. You are equally comfortable working with data scientists on a complex analytical challenge, leading a client workshop, presenting in a boardroom, or developing a commercial proposal.

On the one hand, you can independently scope, develop, validate, and interpret complex models across marketing mix modelling, experimentation, attribution, audience analytics, forecasting, and predictive analytics. You contribute hands-on to technically demanding projects while acting as a trusted advisor to senior client stakeholders. You connect modelling choices and limitations to business implications, explaining not only what the data says, but also how robust the evidence is and what the client should do next.

On the other hand, you identify opportunities to create new products and services based on market developments, client needs, emerging technologies, and reusable analytical methods. Together with colleagues from Data Science, Technology, and Media, you help design, test, and commercialize innovative solutions.

Essentially, you will help our advertisers succeed today, while looking ahead to discover and build the products and capabilities we will need tomorrow.

Your Responsibilities

Advanced Modelling & Methodological Quality

• Lead or contribute hands-on to complex modelling engagements, from problem definition and data assessment through model development, validation, interpretation, and delivery.
• Apply strong statistical and econometric foundations to select, adapt, and implement appropriate methods for complex or ambiguous business questions, including causal inference, time-series analysis, predictive modelling, and optimization.
• Review model code and outputs, troubleshoot analytical issues, communicate assumptions and limitations, and safeguard methodological quality.
• Develop reusable code, methods, and analytical components that strengthen and scale the team’s solutions.