Business Data Scientist, gTech Users and Products

Google's leadership team hand-picks thorny business challenges, and members of BizOps 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. Product Services Analytics (PSA) is a centralized data science and analytics team powering the operations that support Google's global product portfolio. Operating holistically across a broad spectrum of critical service operations, we enable data-backed decision-making that optimizes operational efficiency and drives product improvements. Business Data Scientists in PSA are united by our passion for data science, advanced modeling, and making a scalable impact. We are on a mission to delight the millions of users, developers, and partners who rely on Google's flagship products and services. Being at the intersection of product, engineering, and global operations, we focus on technical solutions across a holistic set of services, including Machine Learning data operations driving AI model advancements, product testing, developer communities, and localization. As a Business Data Scientist, you will balance business and operational needs with technical constraints, develop innovative ML/AI and investigative solutions, and act as a partner to cross-functional teams. You will design scalable solutions to automate analysis, establish performance and modeling, and oversee the data-backed execution of critical operations, driving impact through clear communication. Individual pay is determined by factors including job-related skills, experience, and relevant education or training. Ireland: €90000 - €92000 (EUR) + 15% bonus target + equity + benefits Learn more about benefits at Google . Translate ambiguous business and operational issues into scalable investigative solutions, models, and actionable insights. Lead project work and mentor team members in designing and deploying data science models, ML/AI solutions, robust data pipelines, and evaluation frameworks. Build and implement reusable, scalable investigative tools and automated solutions that optimize the operational processes. Collaborate with engineering partners to write code and implement tools that improve troubleshooting efficiency, operational workflows, and the end-user experience. Manage cross-functional stakeholder expectations, communicating complex technical findings and data insights clearly to both technical and non-technical audiences to drive business decisions. Minimum qualifications: Master's degree in a quantitative discipline such as Statistics, Engineering, Sciences, 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 relevant PhD degree. Experience with Machine Learning (ML) models or Artificial Intelligence (AI). Preferred qualifications: 4 years of experience using analytics to solve product or business problems, coding (e.g., Python, R, SQL), querying databases or statistical analysis, or a relevant PhD degree. Experience applying ML models and AI techniques to analyze complex datasets and solve business problems. Ability to manage multiple projects with engaging priorities in an ambiguous environment. Ability to translate complex data insights into compelling narratives to influence cross-functional partners and executives. Strong foundation in statistical methodologies to analyze operational data and validate business decisions. Master's degree in a quantitative discipline such as Statistics, Engineering, Sciences, 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 relevant PhD degree. Experience with Machine Learning (ML) models or Artificial Intelligence (AI).