Minimum qualifications:
- Master's degree in Science, Technology, Engineering, or Mathematics or a related field or equivalent practical experience.
- 3 years of experience using analytics to solve product or business problems, coding (e.g., Python, R, SQL), querying databases or statistical analysis, or a PhD degree.
Preferred qualifications:
- Experience working with Cloud Analytics products like GCP (Vertex AI) etc.
- Experience with business analytics in building big data pipelines, schemas and data sets, within a media data context.
- Experience using reporting tools (e.g., Tableau, Looker, Power BI) to build business solutions to improve decision-making and execution.
- Experience with marketing data.
- Experience of root cause analysis to ensure that problems are solved at both a tactical and strategic level.
About the job
This role requires a unique blend of Media, Statistics, Machine Learning, AI, Data Infrastructure and Reporting experience and advocate user-centric marketing strategies, demonstrating how Google's products solve real-world problems through unconventional approaches.
In this role, you will be responsible for all things that relate to platform data, script writing and development projects. You will maintain a global scope, and while based out of Dublin, you will be directly supporting global marketing data projects and will serve as a liaison across all regions.
You will play a tactical and technical role in generating marketing data, with a focus on digital marketing data engineering. You will be partnering with internal teams to scope, build and deliver initiatives. You will own the end-to-end program management and delivery of key initiatives that support analytics, reporting, modeling and optimization projects.
Responsibilities
- 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.
- Build and maintain data pipelines, dashboards and business scorecards to help organize data from multiple sources into an easy to digest format that meets functional and business needs.
- Develop QA processes to ensure that data is checked and validated before release to internal teams.
- Manage evaluation frameworks for Machine Learning and LLM models.
- Work with stakeholders to incorporate feedback ensuring that systems, tooling and datasets are usable for defined deliverables and develop detailed delivery plans and communications for key deliverables to keep stakeholders informed.
