Data Scientist II

The Machine Learning (ML) Practice team is a highly specialized customer-facing ML team at Databricks facing an increasing demand for Large Language Model (LLM)-based solutions. we'deliver professional services engagements to help our customers build, scale, and optimize ML pipelines, as we'll as put those pipelines into production. We work cross-functionally to shape long-term strategic priorities and initiatives alongside engineering, product, and developer relations, as we'll as support internal subject matter expert (SME) teams. We view our team as an ensemble: we look for individuals with strong, unique specializations to improve the overall strength of the team. This team is the right fit for you if you love working with customers, teammates, and fueling your curiosity for the latest trends in LLMs, MLOps, and ML more broadly.

The impact you will have:

• Develop LLM solutions on customer data such as RAG architectures on enterprise knowledge repos, querying structured data with natural language, and content generation
• Build, scale, and optimize customer data science workloads and apply best in class MLOps to productionize these workloads across a variety of domains
• Advise data teams on various data science such as architecture, tooling , and best practices
• Present at conferences such as Data+AI Summit
• Provide technical mentorship to the larger ML SME community in Databricks
• Collaborate cross-functionally with the product and engineering teams to define priorities and influence the product roadmap

What we look for:

• 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
• 4+ years of hands-on industry data science experience , leveraging machine learning and data science tools including pandas, scikit-learn, gensim, nltk, and TensorFlow/PyTorch
• Experience building production-grade machine learning deployments on AWS, Azure, or GCP
• Graduate degree in a quantitative discipline (Computer Science, Engineering, Statistics, Operations Research, etc) or equivalent practical experience
• Experience communicating and/or teaching technical concepts to non-technical and technical audiences alike
• Passion for collaboration, life-long learning, and driving business value through ML