Staff Data Scientist

Databricks Databricks · Data AI · San Francisco, CA · Engineering - Pipeline

Databricks is seeking a Staff Data Scientist to help build a data-driven culture and solve product/business challenges using their Data Intelligence Platform. The role involves shaping data science areas like segmentation and recommendation systems, working with cross-functional teams, and building self-serving internal data products. Requires 7+ years of experience in data science/ML, product data science, and proficiency in tools like Spark, Python, and SQL.

What you'd actually do

  1. Shape the direction of some of our key data science areas - segmentation, recommendation systems, forecasting, product analytics, churn prediction and insights.
  2. Work closely with Engineering, Product Management, Sales and Customer Success to understand product usage patterns and trends and make data-driven decisions, recommendations and forecasts.
  3. Manage stakeholders for their focus area - gather changing requirements, define project OKRs and milestones, and communicate progress and results to a non-technical audience.
  4. Mentor and guide junior data scientists on the team by helping with project planning, technical decisions, and code and document review.
  5. Represent the data science discipline throughout the organization, having a powerful voice to make us more data-driven

Skills

Required

  • 7+ years of data science, machine learning, advanced analytics experience
  • Applying Data Science / ML for the end-to-end development and deployment of data-driven products
  • Product data science
  • Collaborating with and understanding the needs of stakeholders
  • Strong coding skills in Scala or Python
  • Software engineering principles (testing, code reviews, deployment)
  • Data analysis and visualization using R and Python
  • Distributed data processing systems like Spark
  • SQL
  • MS or Ph.D. in quantitative fields

What the JD emphasized

  • end-to-end development and deployment of data-driven products
  • product data science
  • customer and user behavior

Other signals

  • customer-facing AI products
  • data-driven product decisions
  • ML for business problems