Top Senior Data Science Jobs in Dayton, OH

As a Data Scientist, you will develop predictive models, analyze geoscientific data, and collaborate with geoscientists to improve mineral exploration efforts.

Lead end-to-end delivery of strategic data science and AI projects: define business problems with stakeholders, prepare large datasets, build and deploy predictive, prescriptive, and generative AI models (including LLMs), implement prompt engineering and fine-tuning, ensure production-grade code and ML lifecycle practices, present insights to senior stakeholders, and promote responsible AI and adoption across teams.

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.

Lead development and deployment of ML/AI solutions for customer success and support: own architecture, ensure Responsible AI and security, drive reliability and observability, mentor teams, partner with engineering/product to deliver scalable, measurable customer value.

Own the analytical function for a business area such as growth, product, AI and automation, or strategic partnerships. Define business problems, metrics, reports, analyses, and experiments; interpret data; and deliver recommendations that influence leadership decisions and business outcomes. The role requires independent analytical execution, strong SQL or Python skills, first-principles problem solving, persuasive communication, and ownership of ambiguous initiatives.

Own the analytical function for a core business area such as growth, product, AI and automation, or strategic partnerships. Define business problems, metrics, reports, analyses, and experiments; surface insights; and drive data-informed decisions with cross-functional stakeholders. The role requires independently structuring data, solving ambiguous problems, communicating findings clearly, and influencing leadership-level decisions using SQL or Python.

Owns the end-to-end development of cybersecurity vulnerability and exploit prediction models, including problem framing, feature engineering, training, evaluation, deployment, calibration, monitoring, and drift detection. The role addresses extreme class imbalance, hierarchical modeling, cold-start customers, and uncertainty quantification. It also requires explaining model outputs to security executives, publishing methodologies and benchmarks, and partnering with engineering to deploy reliable production systems.

Lead development and deployment of machine learning models, perform EDA and data engineering on large datasets, train models including LLMs and unstructured-data solutions, build Power BI dashboards, automate processes, present findings to leadership, and mentor junior data scientists while working with SQL, Python, AWS/Azure, Snowflake, and Salesforce.

Provides biostatistical data science and analytics support for clinical development and evidence generation. Collaborates with biostatistics, data management, clinical development, and medical affairs teams; applies statistics, mathematics, AI, and innovative technologies to clinical trial and real-world data. Contributes to clinical trial design, statistical analysis plans, clinical study reports, publications, and protocol development while following ICH and FDA guidelines.

Own end-to-end data science for a priority vertical (starting with Insurance): frame business problems, build and validate models, partner with ML engineering for deployment, monitor live ROAS performance, and drive measurable revenue and lead quality improvements.

Lead end-to-end data science projects including predictive modeling, algorithm design, and data pipelines. Build campaign-focused models (turnout, partisanship), perform exploration and visualization, translate insights into memos/presentations, collaborate with engineering to productionize models, mentor junior data scientists, and prototype emerging AI solutions for product impact.

Lead development, validation, deployment, and quality control of advanced analytics and ML solutions for fraud detection, waste and abuse, investigative analysis, and program integrity. Provide technical leadership, translate stakeholder requirements, mentor team members, and ensure models are explainable, defensible, and production-ready.

Leads the design, development, deployment, and monitoring of machine learning models for financial applications. Analyzes large structured and unstructured datasets, develops scalable training workflows, and ensures model interpretability, fairness, and regulatory compliance. Collaborates with product, engineering, and business teams to deliver actionable solutions, communicates findings to varied audiences, and mentors junior data scientists. Uses generative AI, MLOps, cloud platforms, and visualization tools to drive business value.

The Senior Data Scientist will lead innovative data science efforts, leveraging advanced analytics, visualization, and insights from complex datasets to boost productivity.

The Senior Data Scientist will partner with multiple departments to deliver impactful data solutions, champion data adoption, and evolve data science standards.

Lead cross-functional data science projects for manufacturing, integrating equipment/process knowledge with analytics. Develop, deploy, and document ML and computer vision solutions within MES and IT/OT ecosystems, qualify automation systems, drive continuous improvement, mentor junior engineers, and present technical findings.

Lead data science strategy across complex customer engagements, turn ambiguous business problems into repeatable ML solutions, define modeling and evaluation standards, drive data readiness and MLOps practices, mentor teams, produce reusable IP, and represent Microsoft in executive and thought-leadership forums.

Embedded on a core business team, own analytics end-to-end: define problems and metrics, pull and analyze data, run experiments, build reports, surface insights, and drive recommended actions with stakeholders to improve product, growth, AI/automation, or partnerships.

Embed with a core business area to define high-impact problems, build metrics/reports, run analyses and experiments, and deliver data-driven recommendations end-to-end. Use SQL/Python to pull and structure data, communicate findings clearly to stakeholders, and drive outcomes across growth, product, AI, or partnerships.

Lead assessment and modernization of NASIC data management: inventory assets, design scalable secure architectures, implement lifecycle governance, ensure data quality and integration, and apply ML/AI and visualization to derive actionable intelligence. Provide recommendations, training, and capacity building for IC personnel.

Lead marketplace measurement, experimentation, and causal analysis; build pricing, recommendation, and retention models; operationalize models with engineering; maintain dashboards and monitor supply-demand balance to guide product and growth decisions.

Lead predictive modeling and causal inference initiatives for clients, developing models from exploratory analysis through production deployment, monitoring, and retraining. Apply GLMs, econometric methods, uplift modeling, propensity methods, and deep-learning forecasting. Process large-scale data with SQL and Spark, build models in Python, improve production reliability, and communicate findings to technical and business stakeholders in a consulting environment.

Build marketing data science models and decision systems for spend allocation, channel measurement, forecasting, experimentation, and growth optimization. Unify marketing, retail, revenue, and product data; develop dashboards and analytical products; apply AI/ML and causal inference; and translate complex analysis into actionable recommendations for executives and cross-functional teams. The role also includes improving data quality, mentoring, documentation, and technical leadership.

Provide analytics and data-driven insights for CoverMyMeds clients, prioritize work that drives commercial outcomes, build relationships with account teams, apply statistical and predictive methods, monitor portfolio health, deliver client-facing visuals and recommendations, support complex requests and escalations, and inform strategic decisions across product, analytics, and operations.

Leads advanced data science and analytics initiatives using statistics, modeling, machine learning, and business analysis. Develops scalable policy-adherence metrics and valuation models using complex structured and unstructured healthcare data, including claims and denials. Creates reports, projections, and presentations; advises executives; manages stakeholders; and influences roadmap and implementation decisions across compliance, contracting, Medicaid, and business teams.