Top Senior Analysis Reporting Jobs in Oklahoma City, OK

The Senior Analyst in Revenue Operations will manage planning cycles, headcount, performance plans, financial modeling, and collaborate with FP&A to support the GTM organization.

As a Senior Data Scientist, you will develop statistical models and machine learning algorithms to improve patient enrollment and trial management while ensuring compliance in a healthcare data environment.

The Senior Data Engineer will design and build scalable data platforms, handle automation for data operations, and ensure reliability and performance of data pipelines while collaborating with various teams.

Owns analytics and reporting that inform sales, merchant success, product, and fraud teams; builds business cases, pricing models, dashboards, and ROI analyses to drive merchant acquisition, expansion, and retention.

Synthesize analyses across product, marketing, reporting, and data science to create executive-ready narratives and decision materials. Partner with analytics teams and business leaders to surface high-impact insights, improve discoverability and adoption of analytics, set standards for storytelling, and ensure insights drive organizational alignment and action.

The Senior Technical Business Systems Analyst improves collaboration and productivity by leveraging tools like Microsoft 365 and AI, optimizing systems, and driving adoption across the organization.

Lead analytics for member engagement and revenue recognition: build and validate Braze attributes, run attribution and A/B test analyses, maintain Looker dashboards, join campaign and engagement data via SQL/DBT, and provide actionable recommendations to Member Operations and Marketing.

Design and scale fraud-mitigation strategies for buyer, seller, and account fraud across Stripe products. Monitor portfolios, identify and predict risky behavior, propose controls, automate workflows, collaborate with Product/Engineering/Data Science, and educate users to minimize losses while enabling growth.

Lead and grow a data engineering team, set technical standards and roadmap, architect scalable GCP data solutions (ingest, transform, warehouse, serving), ensure pipeline reliability and data quality, partner with analytics and client teams, drive best practices (CI/CD, IaC, governance), and evaluate new tooling.

Design, deploy, and govern enterprise-scale data architectures and platforms to support analytics and AI. Define modeling standards, integration pipelines, data quality, lineage, and security. Lead technical direction, mentor engineers, evaluate AI tooling, and support proofs of concept and solution roadmaps that enable generative AI and RAG solutions.