Senior Data Scientist

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

Senior Data Scientist at Databricks to build a data-driven culture by solving product and business challenges, dogfooding Databricks products, and driving future product direction. Responsibilities include usage forecasting, product analytics, user behavior analysis, stakeholder management, and mentoring junior data scientists. Requires experience applying Data Science/ML in production, familiarity with product analytics, strong coding skills (Scala/Python), and proficiency with distributed systems like Spark.

What you'd actually do

  1. Shape the direction of some of our key data science areas for 2020 - usage forecasting, product analytics, user behavior and funnel analysis.
  2. Work closely with Product Management, Sales, Customer Success and other stakeholders to understand product usage patterns and trends and to make data-driven decisions 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 data-scientists on the team by helping with project planning, technical decisions, and code and document review.
  5. Build self-serving internal data products to make data simple within the company.

Skills

Required

  • Experience in applying Data Science / ML in production to build data-driven products for solving business problems.
  • Familiarity with Product Analytics - understanding and tracking customer and user behaviour using lenses like adoption, churn, cohorts and funnel analysis.
  • Experience collaborating with and understanding the needs of stakeholders from a variety of business functions.
  • Strong coding skills in general purpose languages like Scala or Python, and familiarity with software engineering principles around testing, code reviews and deployment.
  • Proficient in data analysis and visualization using tools like R and Python.
  • Experience with distributed data processing systems like Spark and Hadoop, and proficiency in SQL.
  • BS/MS/PhD in Computer Science, or a related field

What the JD emphasized

  • production
  • data-driven products
  • product usage patterns and trends
  • data-driven decisions and forecasts
  • non-technical audience
  • self-serving internal data products
  • data science
  • ML
  • production
  • data-driven products
  • product analytics
  • customer and user behaviour
  • adoption, churn, cohorts and funnel analysis
  • stakeholders
  • Product, Customer Success and Engineering
  • Sales, Marketing and Finance
  • Scala or Python
  • testing, code reviews and deployment
  • data analysis and visualization
  • R and Python
  • distributed data processing systems
  • Spark and Hadoop
  • SQL

Other signals

  • build a data-driven culture
  • solve product and business challenges
  • dogfood Databricks
  • drive the future direction of the products
  • usage forecasting
  • product analytics
  • user behavior and funnel analysis
  • data-driven decisions and forecasts
  • Build self-serving internal data products