Senior Business Data Scientist, AI/ML, Google Cloud

About the role

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.

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.

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).
• 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.

Pay

US: $163000 - $237000 (USD) + 15% bonus target + equity + benefits