Top Senior Analytics Jobs in Columbus, OH

The Director, Research - Power Markets is responsible for leading the QA/QC process for long-term forecasting, advising leadership, and ensuring market insights are effectively integrated into forecasts.

Support product analytics for employee digital products by integrating digital and offline data, building measurement frameworks, creating visualizations and insights, collaborating with product and engineering to translate goals into specifications, and enabling BI capabilities to inform product roadmap and improve employee experience.

Lead analytical monitoring and strategic analysis for homeowners insurance, translate findings into executive recommendations, present to senior leadership, partner across the business to drive profitable growth, and mentor junior team members.

Lead end-to-end quantitative research and segmentation studies using SQL, Python, and statistical methods. Analyze large datasets and surveys, synthesize findings into executive-quality insights, drive process improvements, mentor junior staff, and partner with product and senior stakeholders to integrate research into product development.

Support home lending risk strategy by performing data-driven analyses, identifying trends, monitoring emerging housing and regulatory risks, and delivering clear recommendations and visualizations. Partner with Risk Policy, Product Strategy, and cross-functional teams to design risk segmentation, controls, and performance monitoring to align growth with the firms risk appetite.

Perform end-to-end quantitative analytics for deposit pricing, focusing on CDs and elasticity testing. Analyze large money-movement datasets to inform pricing strategy, build automated interactive dashboards, ensure data accuracy, monitor competitor and macro trends, and present insights to senior leadership while partnering with pricing, finance, risk, marketing, and product teams.

Lead analytics for branch network optimization, new branch and ATM strategy by integrating internal and external data, producing actionable insights, presenting to senior leaders, and partnering cross-functionally to champion analytics best practices.

Perform quantitative analyses of business lending portfolios to generate strategy recommendations, monitor portfolio and collection performance, design and test risk/growth strategies, produce periodic reporting, support ad hoc requests, and maintain audit‑ready documentation and controls.

Lead end-to-end MIS delivery including requirements, data acquisition, ELT/ETL pipeline development, validation, and dashboarding. Build automated pipelines, implement CI/CD/version control, apply advanced analytics (forecasting, anomaly detection, segmentation), enforce data quality and governance, translate insights for executives, prioritize and manage initiatives, mentor junior staff, and ensure audit-ready documentation and reproducibility.

Lead end-to-end analytical projects for alternative lending: define metrics, source and analyze data, design experiments, build reports and present insights to senior leadership to influence product and customer strategies.

Design and implement forecasting and automation solutions for finance. Translate business requirements into development-ready artifacts, collaborate with stakeholders and engineering, monitor solution performance, and document progress in JIRA.

Perform quantitative analyses of business lending portfolios to inform strategy across acquisition, underwriting, portfolio management, collections and recoveries. Develop reporting, test pre/post-implementation impacts, produce actionable recommendations balancing growth and credit risk, and maintain audit-ready controls and documentation.

Perform advanced fraud risk analyses, develop and implement point-of-sale fraud strategies (rules, cutoffs, policies), identify key metrics, collaborate with cross-functional partners, and present findings to senior management to reduce fraud losses while minimizing customer impact.

Lead portfolio-level product operations and change management to drive adoption of AI and analytics tools. Design and run persona-based training, measure adoption and effectiveness, coordinate cross-product priorities, and enable operational efficiency by partnering with HR, analytics teams, and stakeholders to ensure successful AI upskilling and sustained value realization.

Lead definition and execution of enterprise data strategy; design and govern modern data ecosystems (lakehouse, data mesh, knowledge graphs, streaming); build reusable data products and pipelines; ensure compliance with enterprise standards and regulatory requirements; produce architectural artifacts; collaborate across teams to support analytics, reporting, and AI-driven solutions.

Lead a team of analysts and data scientists embedded in sales to build rigorous pilot analyses, create executive-ready business cases, expand consultative analytics across the partner lifecycle, and publish industry insights while coaching and scaling the Solutions Analytics function.

Lead design and build of ETL pipelines and Gold Layer tables in Databricks; develop semantic layers, data dictionaries, and AI-ready documentation; create dashboards and ad-hoc analyses to surface marketing and sales KPIs; partner with stakeholders to translate requirements into analytics-ready datasets and enable self-serve BI and agentic AI workflows.

Lead and scale a casino analytics team to drive data-informed strategy, experimentation, and reporting. Partner with product, data science, and operations to deliver executive insights, build analytical frameworks, and develop high-performing analysts to unlock growth and operational efficiency.

Design and own end-to-end data models and dbt transformations on BigQuery, maintain the semantic layer in Lightdash, govern metrics, enable AI-assisted analytics workflows, partner with stakeholders to deliver trusted reporting, and lead data modeling standards, testing, and documentation.

Help new Dynatrace customers adopt Digital Experience capabilities (Synthetic Monitoring, RUM, Session Replay). Perform data-driven analysis, create concise enablement and investigation deliverables, prioritize customer onboarding activities, drive usage expansion, and collaborate with account teams while communicating findings to technical and business stakeholders.

Lead global Customer Success analytics strategy, architect data and health/adoption models, oversee data pipelines, deliver executive-level storytelling, mentor an analytics team, and drive cross-functional KPI alignment and self-serve BI adoption.

Build AI-augmented BI dashboards, semantic layers, and AI-ready metadata. Use AI-assisted tools to accelerate SQL and dashboard development, productionize ML outputs into BI, enforce data quality and governance, and present insights to business stakeholders including C-suite.

Lead CX analytics strategy and operations by standardizing metrics, enabling self-service analytics, and embedding AI in the analytics lifecycle to enhance decision-making processes.

Own SQL and dbt-based data modeling and data quality for clinical and claims datasets. Author and refactor dbt models/tests, triage DQ alerts, design monitors, optimize SQL performance, and collaborate with Data Engineering and stakeholders to ensure trusted production datasets.

Senior analytics leader who defines analytic strategy, leads a team of SQL analysts and BI developers, architects scalable frameworks for medical economics, risk, population health and value-based care, ensures methodological rigor in DBT/Spark and Quicksight dashboards, drives automation and AI integration, presents to executive stakeholders, and mentors senior analytic talent.