Product Data Scientist, Technology and Engineering

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.

In this role, you will partner with teams across the company to apply Google’s best Data Science techniques to Google’s biggest enterprise opportunities. You will partner with Research, Core Enterprise ML and Machine Learning (ML) Infrastructure teams to build solutions for the enterprise.

The Googler Technology and Engineering (GTE) Data Science team's mission is to transform Google Enterprise business operations, supply chain, IT support and internal tooling with Artificial Intelligence (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 Corp Engineering, and 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. Work with PM, User Experience (UX), and Engineering to contribute to metric-backed annual OKR setting.
  
• Come up with 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.
  
• Convert business problems into unsupervised and supervised ML modeling problems, build these model prototypes from scratch to justify business impact hypothesis.