Senior Business and Marketing Data Scientist
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- Dublin
- Permanent
- Full-time
- Master's degree in a quantitative discipline such as Statistics, Engineering, Sciences, or equivalent practical experience.
- 4 years of experience using analytics to solve product or business problems, coding (e.g., Python, R, SQL), querying databases or statistical analysis.
- Experience with statistical programming in experimental design and data validation.
- Experience managing end-to-end brand measurement frameworks, including brand lift studies, Marketing Mix Modeling (MMM), or Multi-Touch Attribution (MTA)
- 8 years of experience delivering marketing analytics, marketing mix modeling, geo experiments, meta analysis, audience segmentation and propensity modeling.
- Experience working in root cause analysis to ensure that problems are solved at both a tactical and strategic level.
- Experiences with experimental design and supervised/unsupervised machine learning approaches for both regression and classification tasks.
- Understanding of Bayesian approaches and modeling frameworks and applied knowledge of R or Python for statistical analysis and SQL including end-to-end automation of analytics pipelines and workflows.
- Ability to generate practical solutions for marketing analytics problems and use results to drive business change.
- Provide support in media strategy, measurement and optimization that require expertise in advanced analytics work, with special focus on marketing analytics methods and data science.
- Partner with internal teams in advanced analytics work including experimentation, measurement and modeling. Identify patterns and behaviors that are effective predictors of performance and critical drivers for a successful media plan.
- Deliver customer-centric, data-driven approach, based on a people-based marketing strategy to build, segment, and test audiences for best business results.
- Partner with internal teams to scope, build and deliver strategic initiatives driving all things marketing data, with a strong focus on digital marketing data pipelines.
- Develop evaluation frameworks for large-scale models, new metrics, and investigate anomalies. Frame and solve ambiguous problems by scoping technical priorities and innovating on statistical methods.