Senior Data Scientist

At Databricks, we are obsessed with enabling data teams to solve the world's toughest problems, from security threat detection to cancer drug development. We do this by building and running the world's best data and AI infrastructure platform, so our customers can focus on the high value challenges that are central to their own missions.

Founded in 2013 by the original creators of Apache Spark, Databricks has grown from a tiny corner office in Berkeley, California to a global organization with over 1000 employees. Thousands of organizations, from small to Fortune 100, trust Databricks with their mission-critical workloads, making us one of the fastest growing SaaS companies in the world.

Our engineering teams build highly technical products that fulfill real, important needs in the world. We constantly push the boundaries of data and AI technology, while simultaneously operating with the resilience, security and scale that is critical to making customers successful on our platform.

We develop and operate one of the largest scale software platforms. The fleet consists of millions of virtual machines, generating terabytes of logs and processing exabytes of data per day. At our scale, we regularly observe cloud hardware, network, and operating system faults, and our software must gracefully shield our customers from any of the above.

As a Data Scientist on the Data Team, you will help build a data-driven culture within Databricks by helping solve product and business challenges. The Data team also functions as a in-house, production "customer" that dogfoods Databricks and drives the future direction of the products.

If you are interested in machine learning infrastructure, please apply to the Software Engineer Backend job opening here.

The impact you will have:

• Shape the direction of some of our key data science areas for 2020 - usage forecasting, product analytics, user behavior and funnel analysis.
• 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.
• Manage stakeholders for their focus area - gather changing requirements, define project OKRs and milestones, and communicate progress and results to a non-technical audience.
• Mentor and guide data-scientists on the team by helping with project planning, technical decisions, and code and document review.
• Build self-serving internal data products to make data simple within the company.

What we look for: