About the Role
As a Data Scientist on the Data Team at Databricks, you will play a crucial role in building a data-driven culture by solving product and business challenges. The Data team also acts as an in-house, production "customer" that rigorously tests Databricks products, influencing their future direction.
The Impact You Will Have
- Shape the direction of key data science areas such as segmentation, recommendation systems, forecasting, product analytics, and churn prediction and insights.
- Collaborate closely with Engineering, Product Management, Sales, and Customer Success to analyze product usage patterns and trends, making data-driven decisions, recommendations, and forecasts.
- Manage stakeholders within your focus area, gathering evolving requirements, defining project OKRs and milestones, and effectively communicating progress and results to non-technical audiences.
- Mentor and guide junior data scientists, assisting with project planning, technical decisions, and code and document reviews.
- Represent the data science discipline across the organization, advocating for a more data-driven approach.
- Develop self-serving internal data products to simplify data access and understanding within the company.
- Represent Databricks at academic and industrial conferences & events.
What We Look For
- 7+ years of experience in data science, machine learning, or advanced analytics in high-velocity, high-growth companies.
- Extensive experience in applying Data Science / ML for the end-to-end development and deployment of data-driven products to solve business problems.
- Familiarity with product data science, including understanding and tracking customer and user behavior through metrics like adoption, churn, cohorts, segmentation, and funnel analysis.
- Experience collaborating with and understanding the needs of stakeholders from various business functions, including Product, Sales, Engineering, Marketing, and Finance.
- Strong coding skills in general-purpose languages like Scala or Python, coupled with familiarity with software engineering principles such as 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 proficiency in SQL.
- MS or Ph.D. in quantitative fields (e.g., Statistics, Math, Computer Science, Physics, Economics, Operational Research or Engineering).