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.

• 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.

• 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. We work most closely with Product, Customer Success and Engineering at the moment, but also work with the Sales, Marketing and Finance organizations.

• 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.

Databricks is the data and AI company. More than 5,000 organizations worldwide — including Comcast, Condé Nast, H&M, and over 40% of the Fortune 500 — rely on the Databricks Lakehouse Platform to unify their data, analytics and AI. Databricks is headquartered in San Francisco, with offices around the globe.

Founded by the original creators of Apache Spark™, Delta Lake and MLflow, Databricks is on a mission to help data teams solve the world's toughest problems. To learn more, follow Databricks on Twitter , LinkedIn and Facebook .

Databricks is committed to fair and equitable compensation practices. The pay range(s) for this role is listed below and represents the expected salary range for non-commissionable roles or on-target earnings for commissionable roles. Actual compensation packages are based on several factors that are unique to each candidate, including but not limited to job-related skills, depth of experience, relevant certifications and training, and specific work location.

Based on the factors above, Databricks anticipates utilizing the full width of the range. The total compensation package for this position may also include eligibility for annual performance bonus, equity, and the benefits listed above. For more information regarding which range your location is in visit our page here .