About The Job
Engineering Security (EngSec) at Uber is tasked with the cyber-defense of Uber's businesses and assets, and is on a major growth path to deliver the best capabilities for our businesses while advancing the cybersecurity field.
We are looking for a Data Scientist, to join the Data Analytics team, to develop processes and techniques for driving operational and business insight from the entire span of cybersecurity
programs, operations, and projects. This is a great opportunity to join a fast growing EngSec organization in a growth company focused on big bets, and to make a tremendous impact in driving metrics and data rigor in one of Tech's most exciting cybersecurity arenas.
What You'll Do
- Develop an in-depth understanding of Engineering Security's rhythm of business and share strategic insights with senior leadership to influence decision making
- Design, develop, and maintain data pipelines and complex queries using Python and SQL to ensure the accuracy and availability of data
- Build executive-level dashboards on Tableau to facilitate real-time data-driven decision-making and provide actionable insights
- Build and deploy statistical and machine learning models for a range of applications in the Engineering Security space
- Develop executive-level communications and presentations, translating complex data analysis into clear, concise, and impactful insights for Uber's senior leadership team.
Basic Qualifications:
- Undergraduate and/or graduate degree in math, statistics, computer science, engineering or other quantitative fields (or equivalent work experience)
- 4+ years of experience as a data analyst
- Strong proficiency in Python and SQL
- Extensive experience with Tableau or similar data visualization tools
- Strong understanding of statistical analysis and machine learning techniques, with a proven track record of deploying models
- Experience with ML Ops frameworks such as MLflow, Google Vertex AI, or similar.
- Exposure to cloud technologies such as AWS, GCP, or Azure.
Preferred Qualifications:
- Experience implementing LLMs or similar models into data analysis use cases
- Knowledge of additional programming languages such as R or Java