Top Data Science Jobs in Dayton, OH

Build and maintain lifecycle marketing reporting pipelines, dashboards, datasets, and self-service analytics tools. Improve data quality, standardized business logic, workflow reliability, and AI-assisted reporting. Partner with Engineering, Marketing Data Science, Product, and Lifecycle Marketing teams to productionize data products, investigate discrepancies, document processes, and support incrementality measurement through holdouts and experiments.

Lead the development of statistical and machine learning models for time-series and geospatial data. Build analytic solutions, datasets, ETL workflows, forecasting and anomaly-detection models, simulations, visualizations, and reports. Evaluate algorithm performance, optimize classifiers, document processes, and collaborate with engineers, analysts, mission partners, and stakeholders to deliver reliable mission-focused software and data capabilities.

Develops statistical and machine learning models for kinematic behavior detection using live, historical, time-series, and geospatial data. Responsibilities include data integration, ETL, feature engineering, forecasting, anomaly detection, algorithm evaluation, simulation, visualization, reporting, documentation, and stakeholder collaboration. The role also contributes to mission-focused software applications, APIs, data workflows, code reviews, troubleshooting, and agile development in a remote-first environment.

Lead statistical and machine learning model development for time-series and geospatial data supporting the FALCON mission. Responsibilities include feature engineering, forecasting, anomaly detection, ETL, dataset creation, algorithm evaluation, simulation, data visualization, reporting, documentation, and stakeholder collaboration. The role also contributes to software applications, APIs, data pipelines, code reviews, troubleshooting, and iterative agile development.

Leads enterprise data science, AI, machine learning, and advanced analytics strategy supporting NASIC. Designs analytical architectures, optimizes complex data workflows, develops governance frameworks, evaluates cloud capabilities, and guides AI/ML deployment. Produces executive dashboards and progress reports, creates technical policies, and coordinates with data, AI, infrastructure, and intelligence stakeholders. The role requires extensive computational experience, DoD/IC standards expertise, cloud proficiency, and an active TS/SCI clearance.

Develops and deploys AI/ML and intelligent automation workflows for complex intelligence datasets. Builds statistical models, data pipelines, dashboards, and scalable software outputs within secure cloud infrastructure. Integrates data strategies with Zero Trust Architecture, supports reporting and technology prototyping, and collaborates with engineers, database administrators, and product owners. Requires active TS/SCI clearance and onsite support at Wright-Patterson AFB.

Builds and maintains data structures, ETL scripts, dashboards, data visualizations, and automation routines. Performs data cleaning, validation, metadata governance, and basic AI/ML algorithm execution. Supports Agile sprint activities, pipeline documentation, database maintenance, and troubleshooting of ingestion and dashboard issues. Requires onsite work at Wright-Patterson AFB supporting NASIC.

Perform epidemiologic analyses on surveillance, clinical, claims, lab, and survey data; develop analysis plans, statistical methods, and technical reports; build dashboards and automated analytic workflows; design data quality rules and surveillance indicators; translate findings for diverse stakeholders; support public health reporting, manuscripts, and dissemination; mentor junior staff and provide technical assistance.

Leads and develops AI, machine learning, and advanced analytics solutions that deliver customer value. Translates requirements into deployable solutions, identifies and prioritizes projects, collaborates with functional and IT leaders, mentors junior team members, and supports model validation, reproducibility, deployment, and continuous improvement initiatives.

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.

Own product analytics for a healthcare platform by translating business questions into research plans, uncovering insights, modeling feature impacts, defining success criteria, and leading A/B testing. Partner with Product, Data Engineering, Finance, Clinical, and Operations teams to influence roadmap decisions, improve provider experience and quality, and optimize marketplace performance.

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.

Evaluate AI model outputs on data science, statistics, machine learning, experimentation, and quantitative problems. Develop expert prompts, datasets, and reference solutions; identify statistical and methodological errors; and provide structured feedback to improve model performance. The role requires Python, machine learning, statistics, data analysis, experimentation, Pandas, and data modeling skills.

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.

Develop foundation models for longitudinal wearable and physiological data, leading research from hypothesis development through modeling, experimentation, evaluation, prototyping, and product deployment. Build datasets and ML infrastructure, advance representation learning, forecasting, personalization, multimodal and generative modeling, and establish robust evaluation methods. Collaborate with scientists, clinicians, engineers, and product teams while translating research into health experiences.

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.

Own the company-wide revenue retention and add-on monetization analytics strategy. Build causal inference and retention models covering churn, MRR, GRR, NRR, attach, and usage. Partner with Finance, RevOps, Customer Success, CPaaS, AI, and product leaders to establish governed metrics, identify opportunities, guide experimentation, and influence product, pricing, and lifecycle decisions. Set analytical standards, develop reusable methods, and provide technical direction across teams.

Develops scalable data and analytics solutions for BILL’s compliance ecosystem. Responsibilities include extracting and analyzing data, building dashboards, maintaining compliance metrics, automating workflows, troubleshooting systems, performing root-cause analysis, and supporting technology accelerators. The role requires SQL, Python, Excel, business intelligence tools, and experience with risk or compliance systems in payments or financial services.

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.

Build and deploy statistical and machine learning models for the Toast platform. Collaborate with product, engineering, and business teams to identify opportunities, develop data-driven solutions, evaluate models, and communicate insights to stakeholders. Work in a fast-paced environment and contribute to production ML systems and experimentation.

Conduct data science research for microelectronics and hardware security problems, including data collection, preprocessing, exploratory analysis, modeling, and performance evaluation. Apply machine learning and statistical techniques to signal and image datasets, communicate findings through presentations and whitepapers, support research proposals, mentor junior researchers, and collaborate with technical teams and government customers. The role requires work at a secure customer facility in Dayton, Ohio, with occasional travel.

Supports a centralized public health data platform by ingesting, curating, integrating, and validating datasets; maintaining metadata and data dictionaries; applying governance, privacy, deidentification, statistical disclosure limitation, and small-cell suppression standards; and enabling reporting and dashboards through Power BI, ESRI, and Tableau. The role also communicates with technical staff and public health stakeholders, provides technical assistance, and supports secure access to sensitive health data.

Design and analyze rigorous messaging experiments using A/B testing and causal inference. Build measurement frameworks, data pipelines, dashboards, automated analyses, and scalable data solutions for messaging campaigns. Partner with CRM, Product, Algorithms, and Engineering teams to translate ambiguous business questions into actionable recommendations and experimentation standards. Support messaging strategy across channels, timing, content, targeting, and new business verticals.

Develop and productionize machine learning models and predictive analytics using Python, Databricks, PySpark, SQL, and Delta Lake. Build scalable data science workflows, resolve healthcare data quality and entity-resolution issues, and apply Master Data Management practices. Partner with engineering, product, analytics, and business teams to deliver governed, HIPAA-compliant solutions. Perform exploratory analysis, feature engineering, model evaluation, deployment, documentation, and data lineage management for healthcare and member engagement initiatives.

Develop and deploy machine learning models across the full lifecycle, including experimentation, MLOps, monitoring, explainability, and governance. Build scalable model-serving and feature pipelines, contribute to Spark-based data engineering in Databricks and Snowflake, and support orchestration, metadata, security, and compliance practices. Collaborate with platform teams on environment configuration and troubleshooting within HIPAA-governed and FedRAMP-compliant healthcare analytics environments.