Top Data & Analytics Jobs in Tulsa, OK

The Lead Data Scientist will build pricing systems, lead experiments, mentor teammates, and influence strategy across various departments based on data-driven insights.

The Sr. Data Engineer will architect and build an end-to-end data platform, developing data pipelines and infrastructure for AI and robotics teams, ensuring data integrity and governance, and collaborating on ML model development.

The Revenue Operations Analyst will analyze performance metrics, conduct quantitative analysis, automate operations, and ensure data integrity to support revenue teams.

The Quantitative Researcher will execute project work, apply rigorous quantitative methods, lead data collection and analyses, and communicate findings to stakeholders.

The Business Intelligence Analyst analyzes datasets, applies statistical methods, collaborates with teams, develops dashboards, and provides data-informed recommendations to drive business strategies.

The Product Analyst at Pearl will conduct analyses, uncover insights, and provide actionable recommendations to inform product strategy and decision-making. This role involves collaboration with cross-functional teams and utilizing various analytics tools to drive business improvement.

The Senior Data Scientist will lead analytics efforts, develop data models, and improve data pipelines to support Thyme Care's mission in oncology care, while collaborating with stakeholders to enhance performance metrics and reporting.

The Salesforce Business Solution Architect I translates business needs into solutions within Salesforce, capturing requirements, designing processes, and mentoring project teams. Responsibilities include leading projects, defining data and processes, and ensuring use of technology aligns with business goals.

The Credit Analyst II role involves data analysis and MIS reporting for credit risk, focusing on accurate reporting and process improvement.

Design, build, and maintain scalable ETL/ELT data pipelines on Databricks and AWS S3; manage large geospatial and temporal datasets; productionize ML models; implement data validation, testing, and monitoring; optimize storage and processing; document workflows; collaborate cross-functionally.