Top Data Science Jobs in Cleveland, OH

Partner with product engineering to design and analyze experiments, apply causal reasoning and statistical modeling to marketplace data, and drive data-informed product decisions across the engineering org.

Lead the data science strategy for deployed humanoid robots: build predictive maintenance and reliability models, drive fleet performance and RaaS economics analysis, develop anomaly detection and RCA tooling, link manufacturing test data to field outcomes, and set instrumentation, experimentation, and data-quality standards across hardware, software, manufacturing, and operations.

Lead evaluation and data strategy for advanced AI, vision, and perception systems. Define metrics, build evaluation frameworks, audit artifacts, prioritize fixes, and translate model behavior into product decisions and data-improvement loops to ensure production readiness and downstream usefulness.

Analyze microbiome and longitudinal clinical datasets, develop microbiome metrics and biomarkers using statistics and machine learning, create visualizations and reports, draft clinical research documents, support product and study development, and contribute to scientific publications and proposals.

Lead end-to-end data science for a priority vertical (Insurance), building, validating, and deploying ML models that drive ROAS and lead quality. Partner with stakeholders and ML engineering to monitor live performance, iterate models, and identify new modeling opportunities to improve revenue and media efficiency.

Design, develop, and evaluate conversational AI agents and generative AI products. Collaborate with AI scientists, ML engineers, and Responsible AI/Model Validation teams to ensure state-of-the-art, ethical solutions and support deployment and integration of conversational systems.

Develop and refine predictive models, perform exploratory data analysis, and generate AI-driven insights for DoD missions. Integrate multi-source data, apply geospatial analytics, iterate models via Agile sprints, produce performance and cost reports, and optimize analytic workflows for operational deployment.

Leads advanced analytics, machine learning, statistical modeling, and NLP solution development using healthcare and operational data. Builds scalable pipelines, visualizations, and deployed models on Google Cloud Platform, translates findings for clinical and executive leaders, designs experiments, and establishes performance metrics. Requires collaboration across clinical, technical, and data engineering teams, with occasional on-site travel for executive presentations.

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.

Senior Data Scientist partnering with Product, Engineering, and Design teams to analyze user behavior, product performance, and marketplace dynamics. Responsibilities include developing metrics and reporting frameworks, designing experiments, diagnosing business trends, analyzing marketplace behavior, and aligning stakeholders on analytical priorities. The role uses SQL and Python alongside BigQuery, dbt Cloud, Airflow, Tableau, and Statsig, with a focus on product analytics, experimentation, and data-informed decision-making.

The Data Scientist supports customer use of the Epistemix platform through data integration, modeling, and visualization, improving decision-making and customer satisfaction.

Conduct data science research on experimental methodologies and causal modeling, develop tools to analyze experimental data, prototype improvements to platform features, and communicate results to internal teams and external stakeholders to drive product and research decisions.

Build and productionize ML systems for personalization, search, and recommendations. Apply statistical and ML models using Python and SQL, collaborate with stakeholders, and measure business impact across cloud environments.

The Data Scientist will develop data labeling strategies, optimize datasets for LLMs, analyze data quality, and collaborate with engineering on data processing pipelines.

Build lightweight, Slack- and Sheets-integrated AI agents to pull and analyze data from ad and web APIs, surface anomalies and trends, and produce alerts, reports, and LLM-generated content briefs and recommendations. Deliver scoped, fast iterations on multi-step pipelines including classifiers, scorers, and recommendation layers.

Senior individual contributor who drives product analytics, experimentation, and applied modeling. Owns growth and performance analyses, designs experiments with robust statistical evaluation, builds repeatable reporting, and partners with Product, Engineering, Operations, Clinical, and Finance to translate data into actionable product and operational decisions.

Build and scale production Generative AI applications: design multi-agent systems and LangGraph workflows, implement advanced RAG with vector DBs, integrate and fine-tune LLMs, deploy FastAPI async services in Docker, and optimize GenAI performance, cost, and monitoring.

Support IRS projects by developing, validating, and deploying predictive and statistical models on large tax and financial datasets. Perform data cleaning, feature engineering, anomaly detection, and risk scoring using Python/R, SQL, and Databricks. Communicate results to stakeholders and collaborate with cross-functional teams to inform audit prioritization, fraud detection, and compliance efforts.

Design, develop, validate, and deploy statistical and machine learning models to detect fraud, anomalies, and noncompliance in large tax and financial datasets. Clean and feature-engineer data, run robustness and sensitivity tests, collaborate with stakeholders, and present technical and non-technical findings. Support end-to-end data science lifecycle and productionization in a government environment.

Lead and support full-lifecycle data science projects (forecasting, NLP, classification/regression, optimization). Design production-ready solutions within client environments, consult with stakeholders to define ambiguous problems, communicate technical findings, and develop/teach bespoke analytics training.

Support IRS tax compliance and fraud detection by developing, validating, and deploying predictive and statistical models. Clean and analyze large datasets, apply supervised and unsupervised learning (including NLP and graph analytics), collaborate with clients and teams, and deliver technical briefings and defensible, explainable model results.

Work with clients and technical staff to design, develop, and deploy predictive models and data-driven analytical solutions. Perform data extraction, ingestion, and modeling; evaluate and improve models; translate business rules into technical specifications and communicate results to stakeholders.

Support IRS analytics to detect fraud and non-compliance by developing, validating, and deploying predictive and statistical models. Clean and analyze large datasets, perform feature engineering, apply supervised and unsupervised techniques (including NLP and graph analytics), collaborate with stakeholders, and deliver technical and non-technical briefings. Work across the full data science lifecycle and operate in secure government environments.

Develop, validate, and deploy predictive and statistical models using structured and unstructured tax and financial data to detect fraud and non-compliance. Clean and wrangle large datasets, perform feature engineering, apply supervised and unsupervised techniques, and communicate results to technical and non-technical stakeholders. Collaborate with clients and cross-functional teams and support model deployment and documentation in secure government environments.

Leads the design, development, deployment, and monitoring of advanced statistical and machine learning models for insurance pricing, underwriting, claims, fraud, retention, and growth. Advises executives and actuarial teams, establishes modeling and experimentation standards, ensures responsible and scalable productionization, and mentors data scientists. The role also advances MLOps, AI infrastructure, model lifecycle management, explainability, fairness, and data science strategy.