Top Analytics Jobs in Cleveland, OH

Lead the design and operation of automated compliance monitoring for consumer lending: translate regulations into rule-based data checks, own exam and audit data requests, triage exceptions, drive automation, steward compliance data, and provide technical mentorship to a supporting analyst. Produce audit-ready, reproducible evidence for regulators, bank partners, auditors, and senior leadership.

Participate in a multi-week training then rotate across analytics teams (quantitative modeling, model risk, market risk, fraud, commercial analytics, client/employee experience, credit portfolio). Perform data analysis, develop predictive models, produce reports and visualizations, communicate insights to senior stakeholders, conduct market research, and support financial and regulatory reporting.

Lead independent validation and governance of credit and fraud models, build automated Python monitoring for model drift and performance, drive remediation with model developers, and liaise with audit and regulators to support Affirm Bank's model risk management.

Design and maintain people analytics reporting, dashboards, and data models; perform advanced statistical analyses and survey design; automate workflows and collaborate with stakeholders to translate insights into actionable recommendations.

The Data Product Analyst designs and manages data products for insurance domains, ensuring they meet analytical and compliance standards while collaborating with cross-functional teams.

Lead revenue-cycle performance analyses, diagnose root causes, quantify value (ROI/collections lift), and translate findings into pragmatic, stakeholder-ready recommendations. Partner cross-functionally to scope analyses, validate data, and drive strategic workstreams that inform product roadmap and client outcomes.

Lead HR analytics for People & Culture and Capacity & Resource Management: build and maintain dashboards and reports, extract/clean large datasets, deliver talent and capacity insights, run data-driven programs, improve reporting processes, maintain documentation, and mentor team members while collaborating across functions to inform strategic workforce decisions.

Lead industry performance analysis and strategy by managing an analytics team, maintaining syndicated and POS reporting, developing KPIs and forecasts, delivering executive-ready insights, negotiating vendor relationships, and driving data-driven recommendations to optimize distribution, pricing, and marketing.

Lead analytics engineering for Revenue, Marketing, and Finance by building scalable data solutions, data models, and dashboards; provide training and consultation, support ad hoc data needs, and translate technical findings into executive-level insights to improve operational performance.

Lead design and ownership of analytical frameworks and cross-functional reporting for finance and operations. Translate leadership goals into structured analytics projects, standardize metrics and reporting, build business cases, deliver executive-level narratives, and develop predictive models and dashboards to inform strategic decisions across insurance operations.

Administer and provide tier-1 support for Microsoft Dynamics CRM; manage user access, sales pipelines, lead routing, and data hygiene. Build automated reports and executive dashboards in Tableau and Power BI. Partner with digital marketing on targeted campaigns, audience segmentation, A/B testing, and CRM-marketing integrations. Train staff and evaluate third-party CRM tools.

Lead and build a product analytics function: define KPI framework, instrumentation, and self-serve analytics using Pendo and BI tools. Partner cross-functionally to measure product performance, surface insights, influence roadmap and embed AI-powered analytics into product lifecycle.

The Senior Data Engineer will design and implement scalable data models in BigQuery using dbt for analytics and reporting, ensuring data governance and optimal performance.

Summer internship performing data analysis, research, predictive modeling support, and reporting to generate business and risk-management insights. Communicate findings with visualizations, conduct market/peer research, and support financial, regulatory, and business intelligence needs across analytics teams including model risk, fraud, and portfolio management.

Embed with a core business area to define problems, build reports and experiments, analyze data, surface insights, and drive high-impact recommendations end-to-end across growth, product, AI, or partnerships.

Lead analytics for quality and HEDIS performance improvement: design and build reporting, track HEDIS rates and interventions, conduct statistical analysis and predictive modeling, manage data warehouse changes, support audits and special projects, and train stakeholders on analytic solutions.

Lead supply-and-demand forecasting for the psychiatry service line: build and maintain S&D models, integrate models into core data infrastructure, define operational metrics (capacity, clinician utilization, hiring targets), run forecast vs. actual audits, and partner cross-functionally to guide hiring, resource allocation, and operational improvements.

Lead a team to analyze sales data, build and maintain Power BI and Excel reporting, leverage AI and automation for pipeline and lead scoring, align cross-functional GTM strategies, ensure data quality, and deliver actionable, executive-ready insights to drive sales performance and quota attainment.

Lead MyFitnessPal’s product analytics practice and team, advising the Chief Product Officer and senior leaders through actionable data insights. Define product metrics, oversee experimentation, analyze customer and marketing behavior, improve instrumentation and data infrastructure, and shape product strategy, roadmaps, engagement, and retention. This hands-on director role combines executive communication, team development, analytical leadership, and direct analysis across consumer iOS and Android applications.

Design, build, and maintain scalable ETL/ELT pipelines and analytics-ready data models using Python and SQL. Implement GitLab CI/CD, develop dashboards in Tableau/Power BI, perform data cleaning and analysis, support modeling and AI initiatives, document metadata, and mentor junior staff while collaborating with cross-functional teams to deliver actionable insights.

Leads and develops an analytics engineering team that builds data pipelines, analytical models, reporting, and scalable data infrastructure. Partners with Product, Engineering, Growth, and Operations to deliver reliable, actionable data. Establishes governance, quality, testing, documentation, and modeling standards while prioritizing analytics projects and communicating with stakeholders.

Lead analytics and data science initiatives to drive measurable business outcomes across various functions, including Underwriting and Claims. Oversee the full analytics lifecycle, foster stakeholder engagement, and mentor junior team members.

Designs and delivers a global analytics platform supporting reporting, visualizations, self-service analytics, and embedded data products. Responsibilities include data collection and processing, semantic modeling, warehouse and pipeline development, query optimization, governance, analytics enablement, and stakeholder collaboration. The role provides actionable insights from complex travel data and builds integrated analytics applications using cloud platforms and business intelligence tools.

Analyzes healthcare claims, provider, reimbursement, and financial data to support out-of-network dispute negotiations, arbitration, QPA accuracy, and IDR performance. Builds SQL-based reports, dashboards, data processes, metrics, and financial views; validates data quality; integrates internal and external sources; identifies trends and risks; and presents actionable recommendations to leadership and cross-functional stakeholders.

Provide insights and recommendations to improve end-to-end purchase customer journeys across web and app for B2C and B2B. Lead A/B testing and experimentation, ensure accurate data collection and impact measurement, build self-service reporting and visualizations, query and process data, and present findings to stakeholders and senior leadership.