Senior Data Scientist

CSQ225R30

Mission

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 well 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 well 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

• 6+ years of hands-on industry data science experience leveraging typical 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

• [Preferred] Experience working with Apache Spark to process large-scale distributed datasets

Benefits

• Private medical insurance

• Accident coverage

• Employee's Provident Fund

• Equity awards

• Paid parental leave

• Gym reimbursement

• Annual personal development fund

• Work headphones reimbursement

• Business travel insurance