Product Data Scientist, GTE Data Science and ML
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Minimum qualifications:
- Bachelor's degree in Statistics, Mathematics, Data Science, Engineering, Physics, Economics, or a related quantitative field.
- 10 years of experience using analytics to solve product or business problems, performing statistical analysis, and coding (e.g., Python, R, SQL) or 8 years of experience with a Master's degree.
Preferred qualifications:
- Experience with developing at least one deep learning or conventional machine learning model for business impact.
- Experience debugging throughput, latency and response quality issues in AI products, from an analytical perspective.
- Experience managing large-scale data transformation pipelines for batch inference of ML models.
About the job
Help serve Google's worldwide user base of more than a billion people. Data Scientists provide quantitative support, market understanding and a strategic perspective to our partners throughout the organization. As a data-loving member of the team, you serve as an analytics expert for your partners, using numbers to help them make better decisions. You will weave stories with meaningful insight from data. You'll make critical recommendations for your fellow Googlers in Engineering and Product Management. You relish tallying up the numbers one minute and communicating your findings to a team leader the next.
The Googler Technology and Engineering (GTE) team partners with teams across the company to apply Google’s best Data Science techniques to Google’s biggest enterprise opportunities. We partner with Research, Core Enterprise Machine Learning (ML) and ML Infrastructure teams to build solutions for our enterprise.
The GTE Data Science team's mission is to:
- Transform Google Enterprise business operations, supply chain, IT support and internal tooling with AI and Advanced Analytics
- Enable operations and product teams to succeed in their advanced analytics projects through the use of differing engagement models, ranging from consulting to productionizing and deploying models
- Build cross-functional services for use across Corporate Engineering
- Educate product teams on advanced analytics and ML
Responsibilities
- Define and report key performance indicators and launch impact as part of regular business reviews with the cross-functional and cross-organizational leadership team. Translate analysis results to business insights or product improvement opportunities.
- Develop hypothesis to enhance performance of AI products on offline and online metrics through research on techniques around prompt engineering, RAG, supervised finetuning, in-context learning, dataset augmentation, tool-calling efficacy, planning capabilities and feedback loop with reinforcement learning.
- Design and develop ML strategies for data enrichment such as autoencoder based latent variables, complex heuristics etc.
- Evolve variance reduction and simulation strategies to increase reliability of experiments with small sample sizes. Unlock continually improving experimentation with algorithms like contextual bandits.
- Convert business problems into unsupervised and supervised machine learning modeling problems, and build these model prototypes from scratch to justify business impact hypothesis.
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