Top Data Science Jobs in Cleveland, OH

Develops analytical models and applications for U.S. Navy electronic warfare and sustainment systems. Applies data mining, statistical analysis, NLP, machine learning, and data modeling to structured and unstructured data. Integrates logistics, engineering, maintenance, reliability, and operational data to assess readiness, performance, risks, and availability. Creates visualizations, dashboards, reports, and briefings, while collaborating with engineers, logisticians, operations researchers, and program personnel to support lifecycle management and data-driven decisions.

Lead end-to-end data science projects: acquire and prepare messy datasets, develop and evaluate models tied to business outcomes, operationalize models at scale, run experiments, drive ethics/privacy, partner with customers and teams, and coach cross-disciplinary colleagues.

Leads development of statistical and machine learning models for time-series, geospatial, kinematic, and radar data. Responsibilities include feature engineering, forecasting, anomaly detection, data integration, ETL, visualization, algorithm evaluation, simulation, dataset creation, and stakeholder communication. The role also contributes to mission-focused software applications, APIs, data pipelines, documentation, code reviews, and agile development while requiring an active Top Secret or CBP/DHS suitability clearance.

Develops data science models, analytical processes, visualizations, dashboards, and reports for U.S. Navy electronic warfare systems. Integrates logistics, engineering, maintenance, reliability, and operational data to evaluate system readiness, reliability, maintainability, performance, risks, and lifecycle sustainment. Applies data mining, statistical analysis, NLP, and machine learning to support operational decisions and collaborates with engineers, logisticians, analysts, and program personnel.

Own product experimentation, causal analysis, KPI frameworks, predictive modeling, and user segmentation to improve growth, engagement, retention, and revenue. Partner with Product, Engineering, and Design to translate ambiguous questions into rigorous analyses and launch recommendations. Develop lifecycle intervention intelligence, evaluate treatment effects and persistence, communicate uncertainty, and influence roadmap decisions through reusable analyses and clear narratives.

Own company-wide KPI, modeling, and measurement workstreams; develop trusted metrics, KPI trees, forecasts, causal analyses, scenarios, and diagnostic tools. Partner with Data Engineering, Product, Engineering, Finance, and Commercial teams to connect business drivers to retention, monetization, revenue, and margin. Build reproducible SQL and Python workflows, communicate uncertainty and assumptions, and drive adoption of analytical frameworks and recommendations.

Leads company-wide data science priorities, KPI frameworks, measurement standards, causal modeling, scenario planning, and agentic analytical automation. Builds reproducible SQL and Python workflows, validates predictive and causal relationships, establishes trusted metrics, partners with Finance, Product, Commercial, Engineering, and Data Engineering, and mentors scientists. The role drives adoption of governed self-service analytics and communicates assumptions, uncertainty, and business trade-offs to leadership.

Develop and deploy machine learning, AI, causal inference, and statistical solutions for advertising measurement products. Own projects from prototyping through production, conduct experiments, explore large-scale data, and collaborate with product, engineering, analysts, and account teams to improve attribution and marketing ROI.

Build and deploy machine learning models on large healthcare datasets, extract actionable insights, develop scalable data processing systems, collaborate with cross-functional teams, and communicate findings to stakeholders to improve patient outcomes.

Lead analytics for client engagements across data onboarding, study planning, predictive modeling, production deployment, and ongoing optimization. Develop models for audience targeting, segmentation, propensity scoring, and campaign optimization, primarily for commercial pharmaceutical applications. Partner with business, analytics, and IT stakeholders, lead technical workshops, present findings to senior audiences, and manage client relationships. Build machine learning pipelines using Python, SQL, and cloud technologies, while contributing client insights to internal product development.

Develop revenue optimization and risk mitigation strategies using experimentation, causal inference, forecasting, Monte Carlo simulations, and machine learning. Build data pipelines, improve analytics infrastructure, generate insights for client-facing teams, and influence Product and Engineering roadmaps. The role requires strong strategic thinking, business impact orientation, and collaboration in a remote-first environment.

Build and deploy applied data science models for auto insurance carrier matching, media buying, bidding, and ad serving. Own the full modeling lifecycle, including data discovery, validation, deployment, monitoring, and adaptation to late labels, sparse sessions, feedback loops, and market drift. Partner with business stakeholders and an ML engineer to scale production models and deliver measurable acquisition and ROAS outcomes.

Set the technical vision for data science, lead complex statistical and machine learning model development from concept through production, and architect scalable modeling platforms and pipelines. Develop credit risk, pricing, and loss forecasting models; establish model governance standards; partner with senior leaders; mentor data scientists; and communicate technical strategy to executives. The role emphasizes production deployment, monitoring, cloud architecture, and high-impact decision-making across the business.

Leads people analytics research using causal inference, quasi-experimental methods, statistical modeling, and large workforce datasets. Partners with Recruiting, Compensation, Talent, Legal, and business leaders on hiring, retention, promotion, pay equity, quality-of-hire, and organizational effectiveness. Translates research into visualizations and actionable decisions, leads compensation analytics and governance reporting, and uses AI tools to accelerate analysis and synthesis.

The Analytics Manager analyzes complex datasets, builds models, conducts scenario analyses, and provides strategic recommendations to guide decision-making for leadership.

Conduct complex actuarial and predictive analyses supporting insurance product development, pricing, rating, reserving, financial analysis, and process improvements. Develop large-scale predictive models, multivariate analyses, SQL queries, tools, workflows, and business processes. Collaborate with actuaries, product managers, and business teams, communicate insights, improve efficiency, and mentor less experienced staff.

Own Alpaca’s growth data strategy across acquisition, activation, retention, referral, and monetization. Build experimentation frameworks, causal and incrementality analyses, funnel and cohort models, LTV/CAC and churn forecasts, marketing measurement, governed growth metrics, and self-serve analytics. Partner with Product, Marketing, Sales, Engineering, and Finance to influence strategy and roadmaps, communicate insights to leadership, mentor data professionals, and establish standards for growth analytics.

Develop machine learning, AI, statistical, and predictive models using Snowflake and other data sources. Perform data extraction, cleansing, feature engineering, experimentation, visualization, and quality analysis. Partner with business and technical teams to deliver data products, conversational analytics, and actionable recommendations. Lead discovery sessions, communicate insights to nontechnical audiences, mentor analysts, and support enterprise data science projects.

Apply machine learning, artificial intelligence, statistical analysis, and causal inference to large datasets and product analytics. Develop insights and recommendations for recommendation systems and user experiences. Evaluate experiments through planning, monitoring, and post-analysis, build data pipelines and tools, and communicate complex findings to stakeholders to support business decisions.

Develop and deploy data science and AI/ML solutions for scientific, engineering, and business problems within R&D. Responsibilities include data preparation, statistical modeling, machine learning, deep learning, computer vision, generative AI, LLM evaluation, predictive modeling, visualization, and pipeline development. The role collaborates with data engineers, scientists, domain experts, and stakeholders to scale AI solutions and communicate technical findings.

Build and maintain Marketing Mix Models to measure marketing effectiveness, develop attribution metrics, and create optimization solutions for budget allocation and scenario planning. The role applies statistical, Bayesian, time series, regression, and optimization methods using Python and SQL. It partners with marketing and cross-functional teams to translate analytical findings into actionable business recommendations and influence investment decisions.

Own the analytical strategy for a digital primary care program by defining KPIs, building dbt data models, maintaining the Snowflake/Fivetran/dbt stack, and delivering analytics, forecasting, revenue modeling, dashboards, and AI-assisted workflows. Translate patient, EHR, clinical, and operational data into actionable insights for product, care delivery, operations, leadership, health systems, and payers.

Own the data science strategy and experimentation roadmap for ranking and relevance. Partner with product, engineering, ML, and commercial teams to improve marketplace outcomes through ranking models, experimentation, observational analysis, KPI frameworks, and commercial impact measurement. Balance advertiser, user, and platform value; guide launch and investment decisions; communicate findings to stakeholders; mentor scientists; and develop reusable analytical methods.

Develop and maintain predictive models for LTV, churn, retention, segmentation, personalization, and user acquisition decisions. Design experiments and causal analyses, apply LLMs to unstructured data, build AI-powered analytics tools, and productionize models with data engineering partners. Establish modeling standards, communicate insights to stakeholders, and mentor analysts and data scientists.

Lead data science measurement initiatives for media and advertising. Design experiments and causal models to measure incrementality, build marketing mix models, and productionize scalable data pipelines. Develop and deploy models using Python, R, SQL, and cloud platforms while ensuring data quality and privacy compliance. Deliver dashboards, automated reporting, and executive-ready insights, communicate findings to stakeholders, and mentor junior data scientists.