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.
As a Data Scientist with a background and passion for Generative AI, you will contribute to the research, development, and evaluation of our Generative AI solution, and this full-stack solution will be built directly upon Google Play Partnerships' critical data assets and integrated into our core business consultation workflows. You will have a unique opportunity to work closely with mobile games and apps developers, translating data into innovative AI-driven products that directly enable the growth of their businesses.
Google Play offers music, movies, books, apps and games for devices, powered by the cloud. It syncs across devices and on the web. As part of the Android and Mobile team, Googlers working on Google Play do everything from engineering our backend systems, to shaping product strategy, to forming great content partnerships. They make it possible for people to do things like buy an ebook or song on their Android phone, then have it instantly available on their laptop. The Google Play team enhances the Android ecosystem by giving developers and partners a premium store where they can reach millions of users.
Responsibilities
- Design and implement data science pipelines, utilizing techniques such as Retrieval-Augmented Generation (RAG) and fine-tuning, to build advanced Generative AI solutions. Develop algorithms and predictive models to solve business problems.
- Develop and refine Large Language Model (LLM) based agents and workflows, focusing on advanced prompt engineering (e.g., Multi-hop Chain of Thought Prompting (MCP)), configuring RAG systems (e.g., chunking, embeddings, metadata filters, and vector databases).
- Serve as a subject-matter-expert, partnering with consultants and business stakeholders to translate business issues into meaningful data science questions and drive data-driven decision-making.
- Design and implement efficient data pipelines for data collection, cleaning, and pre-processing to ensure the quality of datasets is tailored for LLM training/serving.
Qualifications
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.
Preferred qualifications:
- 6 years of experience using analytics to solve product or business problems, coding (e.g., Python, R, SQL), querying databases or statistical analysis.
- Experience in Generative AI evaluation methodologies and metrics (e.g., LLM-as-a-judge, safety, quality, and bias detection).
- Experience applying Generative AI technologies to enterprise-scale products and solutions, within a quantitative domain.
- Experience with cloud-native platforms (e.g., Google Cloud/GCP).
- Understanding of ML Operations/LLM Operations practices for productionizing AI agents and models.