Top Data Science Jobs in Pittsburgh, PA

The Data Annotator will label and curate data for AI model training, including annotating multimodal data and generating multiple-choice questions. The role requires attention to detail, English proficiency, and data literacy.

The Senior Revenue Operations Analyst will focus on territory management and automation processes within the Revenue Operations team. Responsibilities include analyzing data to provide territory recommendations, systematizing territory assignments, and ensuring data hygiene across various teams. Collaboration with Customer Success and GTM teams is essential to enhance lead distribution and territory alignment.

The Senior Revenue Operations Analyst will shape and assign sales territories through data analysis and automation, collaborate cross-functionally, and monitor data accuracy. Responsibilities include improving internal processes, utilizing tools like Dunn & Bradstreet, and providing guidance to the GTM teams.

As a Senior Data Scientist at Discord, you'll collaborate with various teams to conduct deep analytics, build dashboards, track KPIs, and implement A/B testing to enhance user experience and support product development. You'll be responsible for translating complex data into actionable insights and engaging with both technical and non-technical stakeholders.

The Data Scientist role focuses on applying data science expertise to manage manufacturing data and build automated solutions, primarily using Python and SQL. The candidate will define data initiatives to optimize business processes, work independently on projects, and collaborate with cross-functional teams to drive insights and communicate findings to leadership.

Mentors provide technical support to students by answering queries, reviewing project submissions, and offering personalized feedback while ensuring original work. They maintain a quick response and review time to foster a supportive learning environment.

Responsible for developing tailored data science solutions, leveraging machine learning techniques to transform complex datasets into actionable insights, communicating findings, designing scalable models, and working cross-functionally with teams to integrate data science solutions.