Data Scientist, Cloud Learning Services
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Minimum qualifications:
- 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.
- 4 years of experience in business visualization tools like Looker, Tableau, etc.
Preferred qualifications:
- Master's degree in Computer Science, Economics, or Mathematics, or a related field.
- 5 years of work experience in data science or quantitative analytics with focus on statistical modeling, Machine Learning, and AI.
- Experience with both SQL and Python.
- Experience with Machine Learning, AI or AI Pipeline.
- Experience in Google Cloud Platform.
About the job
Cloud Learning Services (CLS) is revolutionizing direct cloud learning. We empower users of all levels with interactive labs and guided experiences to build practical skills on Google Cloud Platform and other leading technologies. Our mission is to make the cloud accessible, engaging, and enjoyable to learn.
As a Business Data Scientist, you will play a key role in uncovering valuable insights from diverse data sources to solve critical business tests. You will address a wide range of exciting projects, from establishing new measurement frameworks to identifying meaningful patterns in large datasets, ultimately empowering stakeholders to make data-driven decisions.
In this role, you will need to be a detail-oriented problem-solver with a strong foundation in data analytics, data visualization, and Artificial Intelligence/Machine Learning. You will solve the real-world problems and a commitment to continuous learning. Exceptional communication and stakeholder management skills are essential, as you will collaborate extensively with cross-functional teams. If you succeed in dynamic environments and are eager to make a real impact with data, we encourage you to apply!
Responsibilities
- Extract actionable insights from large, complex datasets and build data products like dashboards to operationalize them, driving measurable improvements in Key Performance Indicators (KPIs).
- Present and communicate actionable insights and recommendations to executives, leaders, and cross-functional partners, including Product, Engineering, and Marketing teams.
- Serve as a peer-reviewer and consultant to other members of the team, fostering a collaborative and knowledge-sharing environment.
- Learn and share knowledge of the latest advancements in AI/ML and data science that are relevant to our work.
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