Top Remote Data Science Jobs in Cleveland, OH

Leads product analytics strategy across Xbox experiences, establishing metrics, experimentation, causal inference, forecasting, and machine-learning approaches to guide investments and improve growth outcomes. Partners with senior Product, Engineering, and Business leaders, builds scalable measurement frameworks and AI-enabled analytical capabilities, strengthens telemetry and data quality, and mentors data scientists while setting methodological standards.

Own analytical relationships with travel technology partners, creating scalable reporting on supply, pricing, and performance. Conduct exploratory analysis, apply machine learning and statistical methods, build dashboards, automate recurring workflows with AI tools, and translate complex findings into recommendations for product, engineering, commercial, and executive audiences.

Leads data science research from question framing through modeling, validation, and recommendations. Partners with executives, business leaders, ML engineers, and data engineers to identify opportunities in risk, fraud, retention, pricing, and compliance. Builds forecasts and production-ready models, evaluates data as a strategic asset, and translates insights into product features, offerings, and measurable business outcomes.

Develops predictive models, machine learning solutions, data pipelines, statistical analyses, and internal data applications. The role uses Python and SQL Server to analyze structured and unstructured data, applying NLP, classification, regression, clustering, automation, and AI/LLM techniques. Responsibilities include maintaining data science platforms, supporting cross-functional decision-making, and communicating technical findings to non-technical audiences.

Unpaid remote internship focused on data extraction, scraping, mining, processing, cleaning, pipeline development, AI model training, and containerized microservices. The intern will use Django, Puppeteer, Selenium, Docker, Pandas, NumPy, Scikit-learn, Git, and GitHub while applying testing and data engineering practices. The role requires at least 30 hours weekly and may convert to full-time employment.

Leads development and deployment of statistical, machine learning, and causal inference models for customer decisioning. Designs experiments, estimates treatment effects and incremental value, evaluates targeting performance, and integrates analytical solutions into governed production systems. Partners with business and technology teams, communicates complex findings, and continuously improves models using experimental and operational results.

Lead end-to-end data science for customer-embedded, production solutions: acquire and prepare messy datasets, develop and evaluate models tied to business outcomes, run experiments, deploy operational models at scale, coach cross-disciplinary teams, and drive strategy, ethics, and privacy discussions.

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.

Develop and productionize machine learning and AI solutions across document intelligence, multimodal systems, and foundation models. Responsibilities include dataset and pipeline development, model training and evaluation, GPU experimentation, reusable AI component design, production deployment partnership, monitoring, and communicating technical tradeoffs. The role requires solving open-ended problems in regulated financial services while emphasizing explainability, reproducibility, data governance, reliability, and measurable business impact.

Early-career Data Scientist supporting supply chain transformation through machine learning, forecasting, statistical modeling, data preparation, dashboards, and AI solutions. The role addresses inventory optimization, demand forecasting, production planning, delivery performance, and cost reduction. Responsibilities include developing analytical datasets and workflows with Python and SQL, evaluating models, integrating enterprise data, supporting cloud-based analytics, and communicating insights to business stakeholders in a manufacturing environment.

Build synthetic data pipelines, evaluation systems, benchmarks, verifiers, rubrics, and quality metrics for legal AI models and agentic workflows. Partner with researchers, legal experts, and customers to investigate model failures, validate metrics, maintain data quality and provenance, and convert expert feedback and product usage into training signals and improved evaluations.

Develop marketing effectiveness analytics using Marketing Mix Modeling, attribution, customer lifetime value, experimentation, and causal inference. Build automated, scalable model pipelines, insight-ready datasets, and self-serve dashboards. Analyze campaigns, support marketing strategy, communicate findings to executives, and lead high-impact projects independently while collaborating cross-functionally.

Own analytical strategy and develop AI/ML models for Zip’s financial products, including cash and rent advances. Lead ambiguous 0-to-1 initiatives using bank transaction data, establish evaluation frameworks, guide risk and growth decisions, and deliver models from experimentation through production. Provide technical direction to data scientists, mentor junior team members, collaborate with engineering, and advise senior stakeholders on model trade-offs. The role requires strong financial services, statistical, research, and machine learning expertise.

Build, train, evaluate, and deploy deep learning models for credit modeling and loss prediction. Conduct representation learning experiments, integrate models into ML pipelines, and own projects from hypothesis through measurable business outcomes. Collaborate with data scientists, engineers, and product partners, communicate findings, and develop agentic tools that accelerate data science workflows.

Design and architect secure, scalable healthcare data systems; create core data models using TUVA, OMOP, and FHIR; build production data pipelines and EMR integrations; establish data quality and governance; optimize performance; deploy cloud infrastructure; and ensure HIPAA and HITRUST compliance. The role requires expert SQL and Python or Scala skills, advanced data warehouse and big-data experience, and at least eight years in data science or data engineering.

Develops data quality checks, metadata standards, and AI use-case inventories for HHS. Cleans and integrates complex datasets, applies machine learning and NLP for automated tagging and classification, evaluates AI governance risks, and produces reporting datasets, dashboards, and recommendations. The role requires translating ambiguous federal policy and reporting requirements into repeatable analytical workflows while collaborating with product, design, engineering, and government stakeholders.

Develops machine learning models and production data science pipelines to improve subscription conversion, content recommendations, user understanding, workflow automation, and product decisions. Leads analytical projects and experiments, applies statistical methods to web-scale data, and collaborates with data, product, growth, and engineering teams. The role also communicates complex findings to diverse audiences and helps foster a strong data science culture.

Develop and deploy machine learning and LLM-based solutions for cybersecurity challenges. Responsibilities include analyzing complex datasets, building classification and NLP models, optimizing prompting, embeddings, RAG, and fine-tuning techniques, designing evaluation methodologies, and integrating models into scalable production systems. The role requires collaboration with cybersecurity researchers, software engineers, data engineers, and product managers.

Measures customer engagement impact on pipeline, renewals, account health, and predictive model performance. Conducts baseline, holdout, before-and-after, and control-group analyses; validates customer data and helps tune models. Builds reusable value analyses, dashboards, and reporting for leadership, Customer Success, and Data Science. Requires strong SQL, Python, statistics, experiment design, business communication, and experience translating messy data into trusted insights.

Develop and standardize analytical methods for inferring residence, displacement, and poverty from mobile network operator data. Assess data quality, write technical specifications and tests, guide operator engineering teams, tune statistical methods, and balance accuracy with implementation constraints. Design privacy-preserving analyses, improve data security practices, train internal teams, and support international workshops and emergency-response programs.

The Senior Data Scientist will conduct product analysis, build advanced models and analytics tools, evaluate A/B tests, develop automated dashboards, and generate recommendations for product strategy. The role partners with business stakeholders and Engineering to improve data infrastructure and governance, communicate statistical insights, and support data-driven decisions across Product, Marketing, and Finance.

Own product-led growth analytics across activation, engagement, conversion, monetization, and retention. Lead experimentation, statistical analysis, causal inference, metric development, and proactive opportunity discovery. Build scalable analytical systems, monitoring frameworks, semantic models, and AI-powered tools. Partner with Product, Engineering, and leadership to shape product bets, investment decisions, and roadmap priorities. Work autonomously in a remote-first environment and communicate complex findings through clear, actionable recommendations.

Develop and own advanced machine learning and AI models using bank transaction and cashflow data to enable financial product capabilities, manage risk, and drive profitable growth. Design experiments, build proofs of concept, monitor production models, and collaborate with data and machine learning engineering teams on scalability. Communicate findings and tradeoffs to business stakeholders while applying statistical, research, and financial services expertise.

Build predictive and machine learning models for patient needs, operational forecasting, churn, advocate quality, and risk. Design A/B tests and causal inference studies, apply advanced statistical methods, productionize models with data engineers, and communicate insights to business stakeholders. The role requires Python, advanced SQL, forecasting expertise, statistical proficiency, data privacy awareness, and the ability to work independently in a fast-paced healthcare startup.

Lead computational platform development for biological design, optimization, and functional prediction. Collaborate with wet-lab scientists to validate predictions, oversee data infrastructure and computational workflows, and contribute to technology roadmaps, funding proposals, and partnerships. The role requires developing machine learning methods for biological sequence and structure analysis, translating biological problems into computational solutions, and leading technical teams across computational and experimental disciplines.