Staff Data Scientist

Навыки

• Customer Success
• Анализ данных
• Решения на данных
• Data Lake
• Анализ воронки
• Machine Learning
• Python

О компании и продукте

• Databricks is the Data and AI company. More than 20,000 organizations worldwide — including adidas, AT&T, Bayer, Block, Mastercard, Rivian, Unilever, and 70% of the Fortune 500 — rely on the Databricks Data + AI Platform to build and scale data and AI apps, analytics and agents. Headquartered in San Francisco with 30+ offices around the globe, Databricks offers a unified platform that includes Genie, Lakebase, Agent Bricks, Lakeflow, Lakehouse, and Unity Catalog. To learn more, follow Databricks on LinkedIn , X , YouTube , and Instagram .
• 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 Intelligence 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.

Задачи

• Shape the direction of some of our key data science areas - segmentation, recommendation systems, forecasting, product analytics, churn prediction and insights
• Work closely with Engineering, Product Management, Sales and Customer Success to understand product usage patterns and trends and make data-driven decisions, recommendations 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 junior data scientists on the team by helping with project planning, technical decisions, and code and document review
• Represent the data science discipline throughout the organization, having a powerful voice to make us more data-driven
• Build self-serving internal data products to make data simple within the company
• Represent Databricks at academic and industrial conferences & events

Требования

• 7+ years of data science, machine learning, advanced analytics experience 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 for solving business problems
• Familiarity with product data science - understanding and tracking customer and user behavior using lenses like adoption, churn, cohorts, segmentation and funnel analysis
• Experience collaborating with and understanding the needs of stakeholders from a variety of business functions
• We work most closely with Product, Sales and Engineering at the moment, but also work with the 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 proficiency in SQL
• MS or Ph.D. in quantitative fields (e.g., Statistics, Math, Computer Science, Physics, Economics, Operational Research or Engineering)

Условия

• At Databricks, we strive to provide comprehensive benefits and perks that meet the needs of all of our employees

Паспорт вакансии

История публикации

Откуда что взялось

Отмечено то, что вывели мы. Без пометки — значение назвал работодатель.

Почему на этом месте в выдаче

Порядок выдачи объявлен контрактом: свежесть решает между днями, полнота и зарплата — внутри дня.