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 machine learning and Natural Language Processing (NLP) for developing and deploying AI/ML solutions.
- Experience with 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 application in solving business problems.
- Experience in intelligent self-sustaining agents, including design, development, evaluation, and deployment.
- Experience with cloud platforms (e.g., Google Cloud Platform) and AI/ML services related to LLMs and generative AI.
- Experience in customer support or support-adjacent roles.
- 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
As a Business Data Scientist, 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 US base salary range for this full-time position is $141,000-$202,000 + 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. Your recruiter can share more about the specific salary range for your preferred location during the hiring process.Responsibilities
- Drive customer success at scale while researching and integrating advancements in Large Language Models (LLMs), Generative AI, and AI agent architectures to continuously enhance our capabilities and foster innovation.
- Lead development and deployment of advanced AI/ML solutions, with an emphasis on LLMs and intelligent self-sustaining, agents, addressing business issues.
- Implement 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 and define AI/ML opportunities by collaborating with stakeholders to translate business needs into technical requirements and measurable outcomes.
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