Senior Business and Marketing Data Scientist

Senior Business and Marketing Data Scientist

Google place London, UK

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• 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 Generative AI, Large Language Models (LLMs), or Machine Learning.

• 7 years of business analytics experience building big data pipelines, schemas and data sets, specifically 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 of project managing and building data pipelines, schemas and data sets working with both internal and external partners.
• Experience working with cross functional teams and stakeholders and experience in root cause analysis to ensure that problems are solved at tactical level.
• Experience with marketing data.

Google's leadership team hand-picks thorny business challenges, and members of Biz Ops work in small teams to find solutions. As part of this team you fully immerse yourself in data collection, draw insight from analysis, and then zoom out to develop compelling, synthesized recommendations. Taking strategy one step further, you also persuasively communicate your recommendations to senior-level executives, roll-up your sleeves to help drive implementation and check back-in to see the impact of your recommendations.

• Develop evaluation frameworks for models, new metrics, and investigate anomalies.
• 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 and work with stakeholders to incorporate feedback ensuring that systems, tooling and datasets are usable for defined deliverables.
• Develop detailed delivery plans and communications for key deliverables to keep relevant stakeholders informed. Work with internal and external stakeholders to implement and update processes where necessary.
• Create “self-serve” capabilities within the team for non-technical users and work with the Analytics and Data Science teams to create complex ad hoc datasets where self-service is infeasible.

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