Top Remote Data Science Jobs in Cincinnati, OH

Build Boulevard’s Product Intelligence function by developing product data foundations, pipelines, dashboards, experimentation practices, and customer behavior models. Partner with Product, Engineering, and leadership to deliver actionable insights, self-serve analytics, and productionized models. Establish scalable processes, instrumentation standards, statistical workflows, and data-driven decision-making across the organization.

Develop statistical and machine learning models using soccer tracking and event data to support player recruitment decisions. Build spatio-temporal models, metrics, analyses, and interpretation tools; validate models; communicate findings to professional club clients; and partner with engineering on cloud deployment and production reliability. The role also includes code review, model maintenance, and advancing analytical methods.

Lead data science work embedded in the newsroom: identify high-impact problems, build and productionize predictive and explanatory models, own core metrics/measurement, apply causal inference and experimentation, translate findings for senior stakeholders, and mentor junior data team members to improve analytic standards and adoption.

Own the scientific and product roadmap for monetization data products, including audience creation, expansion, causal measurement, and audience improvement. Lead cross-functional delivery with Product, Engineering, Sales, and GTM; define measurement standards, evaluate trade-offs, support customer pilots, influence commercial decisions, and turn bespoke studies into scalable products. Remain hands-on in SQL, Python, statistical modeling, and causal inference while mentoring scientists and establishing reusable methods.

Own audience or measurement products for Fetch’s monetization business, translating advertiser goals and purchase behavior into predictive models, datasets, and causal studies. Build and validate audience expansion, measure advertising outcomes, assess bias and uncertainty, and improve products using campaign results. Partner with Product, Engineering, Sales, and GTM, present findings to customers, and develop reproducible analyses and production-ready assets.

Develop NLP, LLM, and machine learning pipelines to abstract clinical concepts from structured, unstructured, and multimodal healthcare data. Query healthcare source systems, curate and validate AI-ready datasets, support patient phenotyping and real-world evidence analyses, and deliver HIPAA-compliant data products. Collaborate with product and engineering teams, communicate findings to technical and clinical audiences, and improve data quality for customer AI applications. The role is remote with occasional quarterly travel.

Partner with clients to design healthcare data science solutions, curate multimodal clinical datasets, and conduct statistical, predictive, causal inference, real-world evidence, and AI/ML analyses. Responsibilities include querying EMR and other health data sources, creating statistical analysis plans, managing SQL and analytical programming, ensuring HIPAA-compliant data quality, documenting findings, and presenting results to technical, clinical, and executive audiences.

Lead development of statistical and machine learning models for time-series, geospatial, kinematic, and radar data. Build analytic solutions, training datasets, ETL workflows, forecasting and anomaly-detection models, simulations, visualizations, and performance metrics. Integrate multi-source sensor data, enhance algorithms, document processes, and collaborate with engineers, analysts, mission partners, and stakeholders in an agile environment.

Leads fraud detection, risk analysis, loss mitigation, and fraud strategy development. Builds analytical rules, predictive algorithms, monitoring dashboards, and automated controls using SQL, Python, Tableau, and AI tools. Partners with product, engineering, modeling, and operations teams to define requirements, validate strategies, optimize fraud KPIs, and manage deployments. The role also involves experimental design, governance, onboarding fraud typologies, and mentoring junior team members.

Leads responsible AI and model governance while building and deploying healthcare fraud, waste, and abuse models. Establishes standards for documentation, monitoring, bias, drift, validation, and production readiness. Evaluates generative AI use cases, develops policies, validates large-scale claims models, and presents methodology and controls to partners, auditors, and investigators. Works remotely with data scientists, BI developers, and healthcare subject-matter experts.

Leads and mentors junior developers while designing, modifying, testing, and deploying data solutions in Palantir Foundry. Builds data models and reports from ERP and legacy systems, translates business requirements into technical capabilities, and supports IT modernization. Requires Python, PySpark, cloud architecture, CI/CD, database design, Army domain knowledge, and an active DoD Secret clearance.

Lead design and delivery of scalable ML systems across the full lifecycle for recommendations, demand forecasting, targeting, and personalization. Collaborate with engineers, product, and stakeholders to set architecture, metrics, and production best practices; mentor data scientists; lead experimentation and deploy/monitor models using cloud and MLOps tooling.

Develop and lead data science and machine learning initiatives, including exploratory analysis, predictive modeling, experimentation, recommendation systems, user classification, model monitoring, and production deployment. Partner with product, marketing, analytics, and engineering teams to convert user insights into automated services. Manage projects throughout their lifecycle, support strategic ML planning, and deliver solutions for personalization, cross-selling, and retargeting.

Consult as a fractional data scientist for hedge funds, private equity firms, and family offices. Lead data science initiatives: build infrastructure, develop and deploy models, create portfolio dashboards, monitor post-deployment performance, and partner with investment teams to turn data into actionable investment insights. Advise on vendor selection, data quality, technology stacks, KPIs, and governance.

Build end-to-end data science, machine learning, forecasting, time-series, and AI solutions for supply chain and manufacturing operations. Develop analytical datasets, ETL/ELT workflows, dashboards, APIs, and production models using Python, SQL, cloud platforms, and modern data technologies. Partner with business and technical stakeholders to improve inventory, demand forecasting, delivery, production planning, cost, and operational efficiency. The role owns solutions from problem definition through deployment, monitoring, adoption, and continuous improvement.

Builds a talent network of experienced data scientists for potential future AI evaluation projects. Responsibilities may include creating grading criteria, evaluating AI-generated or human-produced data science work, writing evidence-based justifications, applying consistent judgment, and incorporating reviewer feedback. Candidates should have professional data science experience, strong Python and SQL skills, expertise in statistical modeling, machine learning, experimentation, causal inference, and excellent technical communication.

Lead end-to-end pricing experimentation and strategy for consumer and business products. Design and analyze A/B tests, build pricing models and production-grade data pipelines, partner cross-functionally, and present actionable recommendations to senior leadership.

Develop statistical and machine learning models for clinical trial enrollment, patient matching, forecasting, and trial management. Monitor model performance and bias, build scalable data pipelines using AWS services and dbt, and ensure compliance with HIPAA, GDPR, and privacy standards. Collaborate with ML engineering, product, and clinical research stakeholders to integrate models into practical healthcare solutions.

Drive product-led growth through rigorous analysis of onboarding, activation, adoption, retention, expansion, and monetization. Partner with Product, Engineering, Design, Marketing, and Sales to design experiments, analyze funnels and cohorts, identify behavioral signals, build metrics and dashboards, improve instrumentation, and translate uncertain or incomplete data into actionable product recommendations.

Build and deploy mechanistic, hybrid, and data-driven models for pharmaceutical CMC processes, including drug substance, drug product, packaging, labeling, technology transfer, and manufacturing. Integrate process and experimental data into FAIR data products, analytics, dashboards, and AI-enabled tools. Partner with technical teams and CDMOs to support process understanding, forecasting, risk assessment, regulatory filings, and technology-transfer decisions while owning the digital roadmap and mentoring colleagues.

Develop and deploy machine learning models for manufacturing use cases including predictive quality, machine failure prediction, and time-series pattern discovery. Work directly with customers to scope problems, validate solutions in live deployments, and translate successful analyses into product features. Collaborate with engineering, product, and customer success teams while applying statistics, optimization, generative AI, and agentic AI to deliver end-to-end solutions.

Lead data science engagements with customers to acquire, clean, analyze, and model data; develop scalable operational models and evaluation methods; coach engineers on best practices; drive solution strategy, ethics, and industry thought leadership; write extensible code and collaborate across teams to deliver cloud-based AI solutions.

Own revenue-focused data science from opportunity sizing through production ML. Investigate and measure product changes, build and operate batch and online ML systems for personalization, experimentation, and automation, and partner cross-functionally to integrate models into user-facing products. Establish monitoring, lineage, reproducibility, and security practices; improve data pipelines; mentor data scientists; and define ML development best practices while using AI tools to accelerate delivery.

Design and deploy optimization and machine learning models for allocating scarce parts and resources across competing programs. Responsibilities include formulating MILP and constraint optimization models, forecasting demand and supply risk, building data pipelines, validating outputs, communicating tradeoffs to stakeholders, mentoring junior data scientists, and partnering with software engineers to productionize scalable services.

Lead end-to-end AI/ML initiatives that improve student retention, engagement, enrollment, and re-engagement. Build and deploy predictive models, next-best-action logic, and production MLOps workflows using Databricks, MLflow, dbt, and Dagster. Own experimentation, monitoring, performance measurement, cross-functional implementation, vendor coordination, and executive communication. Mentor teammates and drive measurable outcomes across Product, Engineering, CX, and university partners.