Senior Data Scientist

Cloudflare Cloudflare · Enterprise · Austin, TX · Business Intelligence

Senior Data Scientist role focused on building scalable AI/ML models and GenAI powered application backends, partnering with data and full-stack engineers. The role involves defining, implementing, and training models, and using software engineering best practices to publish insights at scale. Experience with LLMs, frameworks like Langchain/Langgraph, and AI Agents is required.

What you'd actually do

  1. Define, implement, and train statistical, machine learning, deep learning and generative AI models.
  2. Use software engineering best practices to publish model scores/insights/learnings at scale within the company.
  3. Partner and align with business leaders, stakeholders, product managers and internal teams to understand the business and product challenges and goals and address them using predictive analytics in a globally distributed environment.
  4. Understand data landscape i.e tooling, tech stack, source systems etc. and work closely with the data engineering team to improve the data collection and quality.
  5. Active role in hiring, growing, and mentoring the data scientist team in Austin.

Skills

Required

  • Python
  • Spark
  • SQL
  • Tableau
  • Google Analytics
  • BigQuery
  • large language models
  • Langchain
  • Langgraph
  • AI Agents
  • chatbots

Nice to have

  • M.S or Ph.D in Computer Science, Statistics, Mathematics, or other quantitative fields
  • 2+ years experience with a fast-growing SaaS business based company
  • Experience in hiring data scientists and establishing team best practices

What the JD emphasized

  • AI-native curiosity
  • building scalable, reliable AI/ML models, services and GenAI powered application backends
  • partnering closely with data and full-stack engineers to deliver new features and operate the pipelines and platforms behind our products
  • incorporate strong AI components
  • Define, implement, and train statistical, machine learning, deep learning and generative AI models
  • Use software engineering best practices to publish model scores/insights/learnings at scale within the company
  • Proven track record of applying data insights and machine learning in order to address business needs and drive revenue
  • Proficiency in large language models and the frameworks (Langchain, Langgraph, etc.) necessary for implementing GenAI applications, such as AI Agents, chatbots and related use cases

Other signals

  • building scalable AI/ML models
  • GenAI powered application backends
  • implement and train statistical, machine learning, deep learning and generative AI models
  • implementing GenAI applications, such as AI Agents, chatbots