Top Senior Level Data & Analytics Jobs in Washington DC,

Looking for a Senior Oracle Database Administrator with Oracle Goldengate 21c Microservices experience to deploy and maintain database clusters, automate tasks, optimize code, and participate in on-call rotation. Must have experience in Oracle 12c/19c, Data Guard, performance tuning, and PL/SQL programming.

The Staff Data Engineer will lead the development of scalable data architectures and pipelines, mentor junior engineers, build ETL/ELT processes, manage data warehouses, and implement cloud-native solutions while ensuring robust data governance and performance optimization.

The Staff Data Scientist will develop and deploy advanced statistical and machine learning models, focusing on forecasting and optimization to drive business growth. Collaborating with cross-functional teams, they will analyze complex datasets, conduct A/B testing, and communicate findings to support strategic decision-making.

As a Staff Data Analyst at Vercel, you will transform data into actionable insights, establish key business metrics, automate reporting, collaborate with stakeholders, and develop analytics models to drive company growth and operational efficiency.

The Senior Analyst will provide insights into the data center industry through market trend analyses, competitive intelligence, and data analysis. Responsibilities include monitoring competitors, analyzing industry reports, and collaborating with various departments to support strategic decision-making and growth initiatives.

The Senior MLOPs Engineer will architect and optimize ML inference platforms, handling all aspects of development and operations. They will work with cross-functional teams to achieve business objectives through engineering solutions, mentor team members, and ensure the effective deployment of models via scalable inference systems.

The Senior Data Scientist will develop and deploy lead scoring and recommendations models to increase sales call conversion rates, work with business stakeholders to prioritize data modeling projects, establish best practices for model performance evaluation, mentor team members on ML projects, and guide data modeling needs for machine learning systems.