Top Data & Analytics Jobs in Cincinnati, OH

The Marketing Data Scientist will analyze data, onboard clients, develop data pipelines in R, and propose improvements to enhance efficiency.

As a Data Scientist, you will analyze blockchain data to derive insights, develop and deploy predictive models, and improve product offerings.

Build reliable data models and semantic layer for AI-powered analyses. Support Product, Finance, and Customer Success with SQL-based analytics, BI dashboards, and stakeholder storytelling. Standardize data team processes, prioritize work, and collaborate across company to enable data-informed decisions.

The Senior Data Engineer will design and maintain data pipelines, write production-grade Python and SQL code, and support AI initiatives while collaborating across teams.

Lead the technical roadmap and hands-on development of a scalable data platform, build analytics and data access solutions, ensure data quality/governance/observability, mentor and grow a team of data engineers, collaborate cross-functionally, and evaluate new big-data technologies.

Entefy seeks a Senior Data Scientist with a Master's or PhD in a relevant field, and 3+ years of experience. Responsibilities include delivering advanced analytics, leading teams, and extracting insights from datasets.

The Sr. Data Engineer - Databricks designs and maintains scalable data solutions using Apache Spark and Databricks, ensuring data quality and performance for analytics and reporting.

Lead design and delivery of large-scale quantitative and qualitative market research studies, manage client accounts, develop RFPs, mentor research managers, ensure project quality, timelines, and provide strategic recommendations and reports.

Analyze historical and real-time staffing and appointment data to forecast resource needs, build staffing plans, create reports/dashboards and process maps, and collaborate with leadership, HR, and training to optimize scheduling and staffing levels.

The Senior Analytics Engineer will build and maintain data foundations for corporate reporting, translating business requirements into data models, ensuring consistent metric definitions and investigating data discrepancies for reliable reporting.