Data Scientist

Doximity•San Francisco, CA

12d•$110,000 - $150,000

About The Position

The Data Scientist works within cross-functional delivery teams alongside data scientists, engineers, and product managers to discover data insights that help improve healthcare. The role leverages Doximity’s datasets to identify and classify behavioral patterns of medical professionals on the platform, and contributes to product and client-facing analytics. The Data Scientist works with product leaders and managers to inform data team strategy and participates in execution and some planning. The role also collaborates with product managers, analysts, and developers to lead data projects from data ingestion through analysis and recommendations. The role can be based at Doximity’s San Francisco headquarters or remotely in the U.S. Anticipated total compensation is $110,000–$150,000, depending on experience, skills, location, and internal equity. The package may include base salary, equity, and annual bonus or incentive compensation where applicable.

Requirements

At least 2 years of professional experience as a data analyst or data scientist.
Knowledge of statistical concepts, especially exploratory data analysis techniques and probability theory.
Excellent SQL skills, including creating and evaluating complex statements involving numerous tables and data relationships.
Excellent data visualization and storytelling skills to explain results and solutions clearly and compellingly to stakeholders.
Proficiency with Python data analysis libraries such as Pandas and NumPy, and a basic understanding of object-oriented programming concepts as they relate to Python.
Prior exposure to distributed data processing concepts and execution, such as working with column stores or leveraging Spark.
Ability to learn quickly, with curiosity about and passion for data.

Responsibilities

Use Doximity’s datasets to identify and classify behavioral patterns of medical professionals on the platform.
Create product and client-facing analytics.
Work with product leaders and managers to inform data team strategy, and participate in its execution and some planning.
Collaborate with product managers, analysts, and developers to define and lead data projects from data ingestion through analysis and recommendations.