Business Data Scientist, AI/ML, Google Cloud

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 focus on Machine Learning (ML) and Natural Language Processing (NLP) for developing and deploying AI/ML solutions.
• Experience with relevant AI/ML 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 in customer support or support-adjacent role.
• Understanding of cloud platforms (e.g., Google Cloud Platform) and their AI/ML services, particularly those related to LLMs and generative AI.
• 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.

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 AI/ML solutions, translating data into actionable strategies.The Canada base salary range for this full-time position is CAD 144,000-148,000 + bonus + equity + benefits. Our salary ranges are determined by role, level, and location. Within the range, individual pay is determined by work location and additional factors, including job-related skills, experience, and relevant education or training.

• Develop predictive, personalized, and proactive customer support solutions to drive customer success at scale while researching and integrating advancements in Large Language Models (LLM), generative AI, and AI agent architectures to continuously enhance the capabilities and foster innovation.
• Lead the development and deployment of advanced AI/ML solutions, with an emphasis on LLMs and intelligent autonomous agents, addressing business issues.
• Implement evaluation frameworks and metrics for LLMs and AI agents, encompass 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, identify and address model drift, and ensure system stability and scalability.
• Identify and define AI/ML opportunities by collaborating with stakeholders to translate business needs into technical requirements and measurable outcomes.