Senior Data Scientist

Job Description:

Dentsu is transforming advertising as a force for growth and a force for good. It has become the destination for employees to cultivate meaningful careers and for brands to accelerate growth like never before. Media is the largest area of specialism within dentsu international, and is brought to markets globally through three award-winning agency brands: Carat, iProspect and dentsu X. All three are underpinned by a scaled network offering of talent, capabilities, and services to support, grow and transform the world’s leading advertisers. Working together as the most integrated network, dentsu Media supports Carat’s EQ and people-first approach, dentsu X’s experience-driven proposition and iProspect’s full-performance methodology. dentsu Media also allows greater access to unparalleled integrated, marketing solutions through the network’s Creative and CXM specialisms.

The Senior Data Scientist, will be part of a cross-functional team, specifically focused on developing mixed marketing models for our clients. The ideal candidate is both well versed in the data modeling process and leading MMM projects: whether it’s data collection, data tidying, statistical modeling, and communicating insights to non-technical teams.
This job requires the candidate to be proficient in Advanced Analytics concepts such as marketing mix modeling, regression, etc.

Responsibilities:

• Collaborate with the project lead to understand client objectives and design robust statistical and econometric attribution models.

• Develop, validate, and refine statistical and econometric models, including Marketing Mix Models, to measure marketing effectiveness across channels.

• Build statistical analyses aligned with business needs, focusing on ROI, marginal returns, and budget optimization.

• Utilize open-source MMM tools such as Google Meridian, Meta Robyn, and Light-weight MMM, while exploring custom modeling approaches when necessary.

• Explore and implement a variety of statistical and analytical techniques, including linear and non-linear regression, Bayesian time-series forecasting, hierarchical models, and machine learning algorithms (e.g., random forests, gradient boosting).

• Prepare and manage structured and unstructured data sources, ensuring data quality through effective ETL processes and rigorous validation checks.

• Automate data pipelines and model workflows using tools like SQL, Python, dbt, or Airflow to improve efficiency and scalability.

• Conduct scenario simulations, what-if analyses, and incrementality tests (e.g., geo-lift, A/B testing) to validate model outputs and improve attribution accuracy.

• Work closely with the project lead to translate complex analytical outputs into actionable insights and strategic recommendations for clients and industry stakeholders.