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Responsibilities:
Build and increase customer data science workloads and apply the best MLOps to productionize these workloads across a variety of domains.
Develop LLM solutions on customer data such as RAG architectures on enterprise knowledge repos, querying structured data with natural language, and content generation.
Advise data teams on several data science topics such as architecture, tooling, and best practices.
Provide technical mentorship to the larger ML Subject Matter Expert community.
Requirements:
6 years of hands-on industry data science experience, using typical machine learning and data science tools including pandas, MLflow, scikit-learn, gensim, nltk, and TensorFlow/PyTorch.
Experience building production-grade machine learning deployments on AWS, Azure, or GCP, including drift monitoring.
Experience with the latest techniques in natural language processing including vector databases, fine-tuning LLMs, and deploying LLMs with tools such as HuggingFace, LangChain, and OpenAI.
Graduate degree in a quantitative discipline (Computer Science, Engineering, Statistics, Operations Research) or equivalent practical experience.
Experience communicating and teaching technical concepts to non-technical and technical audiences alike.
Passion for collaboration, life-long learning, and driving value through ML.
Experience working with Apache Spark to process large-scale distributed datasets.
Experience working with the Databricks platform.
5+ years of customer-facing experience in a pre-sales or post-sales role.
Can meet expectations for technical training and role-specific outcomes within 3 months of hire.
Experience
4-6 yrs
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