Business Data Scientist, AI/ML, Google Cloud
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Minimum qualifications:
- Master's degree in a quantitative discipline such as Statistics, Engineering, Sciences, or equivalent practical experience.
- 3 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 with relevant ML/AI libraries (e.g., TensorFlow, PyTorch, scikit-learn, Hugging Face).
Preferred qualifications:
- PhD degree in Computer Science, Artificial Intelligence, Machine Learning, or a related quantitative field.
- Experience with Large Language Models (LLMs), including their application in solving business problems.
- Experience in intelligent autonomous agents, including their design, development, evaluation, and deployment.
- Experience with cloud platforms (preferably Google Cloud Platform) and their AI/ML services, particularly those related to LLMs and generative AI.
- Experience in Customer Support or Support-adjacent role.
- Excellent programming skills in Python or a similar language with the ability to translate data into actionable insights and communicate findings to technical and non-technical stakeholders.
About the job
In this role, 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 datasets to develop and deploy innovative ML/AI solutions, translating data into actionable strategies.Responsibilities
- Developing predictive, personalized, and proactive customer support solutions to drive customer success at scale while researching and integrating advancements in LLMs, generative AI, and AI agent architectures to continuously enhance our capabilities and foster innovation.
- Lead the end-to-end development and deployment of advanced AI/ML solutions, with an emphasis on Large Language Models (LLMs) and intelligent autonomous agents, addressing business issues.
- Implement evaluation frameworks and metrics for LLMs and AI agents, encompassing both traditional model performance and agent-specific evaluation criteria (eg. 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 and define AI/ML opportunities by collaborating with stakeholders to translate business needs into technical requirements and measurable outcomes.
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