Staff Data Scientist - Data Science Platform

Key Skills

Apache, Databricks, Lakehouse, Machine learning techniques, MLflow, Python Programming, SQL

Job Description

At Databricks, we are inspired by helping data teams solve the world's toughest problems. 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.

We develop and operate foundational platforms for Data Scientists and Engineers, ranging from data management, metrics, forecasting and anomaly detection infrastructure, to measurement and experimentation, and beyond. Our offering is evolving at fast-pace as we build the Databricks Data Intelligence Platform, empowering our customers with an outstanding unified platform for data, analytics, and AI.

As a Data Scientist on the Data Team, you will help build a data-driven culture within Databricks by helping define, measure and lead top company priorities. The Data team also functions as an in-house, production "customer" that feeds Databricks and guides the future direction of the products.

You will report directly to the Manager, Data Science (Engineering).

The impact you will have:

• You will empower our world-class Data Team by building scaled and automated solutions for product analytics.
• You will empower Bricksters to use insights on product usage patterns to make decisions.
• You will shape the direction of main data science areas such as North Star metrics, segmentation, recommendation systems, anomaly detection, and experimentation.
• You will gather changing requirements, define project OKRs and milestones, and communicate progress to both technical and non-technical audiences.
• You will guide junior data scientists on the team by helping with project planning, technical decisions, and code and document review.
• You will represent the data science discipline throughout the organization, having a powerful voice to make us more data-driven.
• You will represent Databricks at academic and industrial conferences and events.

What we look for:

• Engineering and data science skills, allowing you to not only understand data, analytics and AI pain-points, but also build scalable solutions to those problems.
• The ability to work with analytics and engineering professionals, reducing silos to exceed what either side could achieve independently.
• 7+ years of data science, machine learning, advanced analytics experience in high velocity, high-growth companies
• Experience deploying Data Science / ML solutions in production for achieving results.
• Coding skills in Python and SQL
• Experience with distributed data processing systems like Spark and familiarity with software engineering principles around testing, code reviews and deployment.
• Masters or higher in quantitative fields.

Pay Range Transparency

Databricks is committed to fair and equitable compensation practices. The pay range(s) for this role is listed below and represents base 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 utilizes 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.

Local Pay Range

$192,000—$260,000 USD

About Databricks

Databricks is the data and AI company. More than 10,000 organizations worldwide

• including Comcast, Condé Nast, Grammarly, and over 50% of the Fortune 500
• rely on the Databricks Data Intelligence Platform to unify and democratize data, analytics and AI. Databricks is headquartered in San Francisco, with offices around the globe and was founded by the original creators of Lakehouse, Apache Spark™, Delta Lake and MLflow. To learn more, follow Databricks