Data Scientist, gTech Ads
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Minimum qualifications:
- Bachelor's degree in Statistics, Data Science, Mathematics, Physics, Economics, Operations Research, Engineering, or a related quantitative field.
- 1 year of experience in a data science field.
Preferred qualifications:
- 2 years of experience using Python, SQL to solve messy, open-ended business problems.
- Experience taking Machine Learning (ML) insights directly to customers or stakeholders including initial problem scoping and model selection to interpretation and driving business action.
- Understanding of statistical models and their application in a commercial context.
- Understanding of statistical algorithms typically used in marketing analytics e.g. experimentation (A/B testing), causal inference, and attribution modeling.
About the job
gTech Ads is responsible for all support and media and technical services for customers big and small across our entire Ad products stack. We help our customers get the most out of our Ad and Publisher products and guide them when they need help. We provide a range of services from enabling better self help and in-product support, to providing better support through interactions, setting up accounts and implementing ad campaigns, and providing media solutions for customers business and marketing needs and providing complex technical and measurement solutions along with consultative support for our large customers. These solutions range from bespoke and customized ones for our customers to scalable support for millions of customers worldwide. Based on the evolving needs of our ads customers, we partner with Sales, Product and Engineering teams within Google to develop better solutions, tools, and services to improve our products and enhance our client experience. As a cross-functional and global team, we ensure our customers get the best return on investment with Google and we remain a trusted partner.
Responsibilities
- Collaborate with internal and external stakeholders to unpack their issues and identify the best statistical techniques that can solve the issue; collaborate with colleagues on the development of end-to-end modeling framework.
- Own the full data science lifecycle from raw data extraction and model development to delivering the final solution. Ensure the technical excellence of the math and the business relevance of the output.
- Transform model results into "so-what" insights. Co-present findings to clients and work alongside them to ensure recommendations are integrated into their actual business processes.
- Contribute to the development of standardized, scalable modeling frameworks. Collaborate with DS peers to ensure our solutions aren't just one-offs, but can be scaled across multiple client engagements.
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