Minimum qualifications:- Master's degree in Statistics, Engineering, Sciences, a related quantitative discipline, 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:- Master's degree or PhD in Computer Science, Artificial Intelligence, Machine Learning, or a related quantitative field.
- 5 years of experience in a data science role, with a specific focus on machine learning and Natural Language Processing (NLP) for developing and deploying AI/ML solutions.
- Experience programming in Python or a similar language, along with relevant ML/AI libraries (e.g., TensorFlow, PyTorch, scikit-learn, Hugging Face).
- Experience with Large Language Models (LLMs), including their application in solving business problems.
- Ability to translate complex data into actionable insights and communicate findings to technical and non-technical stakeholders.
About the jobAs a Data Scientist, you will focus on Artificial Intelligence to join our team. You will be instrumental in driving customer success at scale by building the predictive, personalized, and proactive solutions that define the future of customer support. You will work with large datasets to develop and deploy innovative ML/AI solutions, translating complex data into actionable strategies.
Individual pay is determined by factors including job-related skills, experience, and relevant education or training.
US: $163000 - $236000 (USD) 15% bonus target equity benefits
Learn more about benefits at Google .
Responsibilities - Drive customer success at scale by developing predictive, personalized, and proactive customer support solutions.
- Lead the end-to-end development and deployment of advanced AI/ML solutions, with a strong emphasis on Large Language Models and intelligent autonomous agents, addressing complex business challenges.
- Implement robust evaluation frameworks and metrics for LLMs and AI agents, encompassing both traditional model performance and agent-specific evaluation criteria (e.g., task completion rate, reasoning quality).
- Monitor and maintain deployed LLM and AI agent solutions in production, including tracking key performance indicators, identifying and addressing model drift, and ensuring system stability and scalability.
- Identify AI/ML opportunities by collaborating closely with stakeholders to understand business needs and translate them into technical requirements and measurable outcomes. Proactively research and integrate advancements in LLMs, generative AI, and AI agent architectures to continuously enhance our capabilities.
