Top Senior Machine Learning Jobs in Cincinnati, OH

Lead design and scaling of a unified fraud and risk platform. Own roadmap, fraud metrics, SQL-based analysis, A/B testing, real-time detection models, and cross-functional delivery to balance customer experience and risk mitigation.

Lead fraud detection and mitigation for a conversational BNPL product. Define fraud typologies, own repayment and chargeback analytics, manage fraud KPIs, design operational policies, collaborate on ML models and decisioning workflows, and coordinate SARs and compliance reporting.

Lead fraud detection and mitigation for a conversational Send Now Pay Later product: define first-party fraud typologies, own repayment and chargeback analytics, manage fraud KPIs, design operational policies, integrate ML and decisioning tools (Taktile), and coordinate SARs and compliance reporting.

Lead credit analytics for the Send Now Pay Later product: build and maintain credit policies, pricing models, vintage and NPV analyses, executive reporting, model integration and testing, and explore alternative data to optimize approval rates and portfolio health.

Lead credit analytics for the Send Now Pay Later product: build and optimize valuation, vintage and NPV models; manage credit policies, portfolio monitoring, and NPL reporting; integrate and test scoring models with engineering/data science; and research alternative data to improve predictive performance.

Lead fraud detection and mitigation for a conversational BNPL product: define fraud typologies, analyze repayment/chargeback trends, own fraud KPIs, design policies, collaborate on ML models and decisioning workflows, and coordinate SARs with Compliance.

Lead fraud risk for a conversational lending product (SNPL): define first-party fraud typologies, analyze repayment and chargeback trends, own fraud KPIs, design automated/manual blocking policies, coordinate ML/rules with central Fraud and Data teams, and liaise with Compliance on SARs and loan abuse.

Lead analyses measuring campaign impact, build tagging models (prompt engineering and ML), synthesize insights across programs, analyze experiments including RCTs, and produce presentations and post-campaign reports for internal teams and clients.

The role oversees analytical characterization and comparability studies for biologics, ensuring regulatory compliance and leadership in product development.

The Engineering Manager for Smart Signals leads a team, manages execution, and maintains technical depth in backend systems for fraud detection. They shape strategy and coordinate across teams to enhance product quality.

Lead development of digital twin and simulation models for warehouse operations, deploy AI-driven decision systems, build forecasting and optimization models, validate experiments linking operational metrics to financial impact, and partner with engineers, operations, and external AI/academic partners to scale innovations across fulfillment networks.

Design and build backend systems and APIs for AI-driven products, ensuring scalability, reliability, and integration of ML models while collaborating with engineering and product teams.

The Senior Manager will oversee the fraud lifecycle, optimize detection strategies, conduct fraud reviews, collaborate with teams, and manage risk reporting.

Design, optimize, and scale ClickUp's search infrastructure using OpenSearch/ElasticSearch. Improve relevance, speed, and real-time indexing; build vector/semantic search capabilities; create measurement frameworks; integrate search with AI and product features; troubleshoot at scale; and mentor junior engineers to maintain high search quality under heavy load.

Design, scale, and optimize ClickUp's search infrastructure using OpenSearch/Elasticsearch. Improve relevance, speed, and real-time indexing, build vector search capabilities, create evaluation frameworks, and collaborate with AI/backend/product teams to integrate and troubleshoot search at scale.

Lead North America revenue growth by building relationships, managing pipeline, responding to RFPs/RFIs, coordinating cross-functional teams, tracking activity in Salesforce, representing Optibus at industry events, and delivering data-driven sales forecasts.

Lead end-to-end delivery of LOIS by owning the master program plan, coordinating cross-functional teams (engineering, product, design, ML, data, SRE, operations), resolving blockers and dependencies, establishing lightweight delivery infrastructure and tooling, partnering on release planning, and maintaining transparent status, risks, and accountability to deliver complex features to customers.

The Principal Data Scientist will design, develop, and deploy machine learning models, innovating methodologies and collaborating across teams to enhance financial decision-making and risk management.

The Senior AI Data Engineer will design and build scalable data pipelines, ensure data quality, and support AI product development through a unified analytical data framework.

As a Senior Data Scientist, you will identify business opportunities, build machine learning solutions, partner cross-functionally, and translate analyses into actionable insights.

The Senior Backend Data Engineer will extend data ingestion and processing pipelines, build APIs and backend services, and provide technical leadership.

Lead supply chain analytics engagements: manage client teams, design data-driven solutions for demand forecasting, inventory and network optimization, drive key initiatives to closure, and provide thought leadership in supply chain analytics.

Lead Tekmetric's API product strategy and roadmap for the SaaS platform. Own API lifecycle (contracts, versioning, documentation), drive developer experience and adoption, identify integration opportunities, lead generative AI initiatives, perform user research and analytics-driven prioritization, collaborate across design/engineering/sales, train internal teams, and measure business impact.

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.

Drive new business and expand accounts by selling AI-driven services and software to engineering and manufacturing customers. Manage full sales cycle from prospecting to close, align solutions to measurable ROI, collaborate with technical and marketing teams, and meet revenue targets while attending industry events and maintaining CRM pipeline accuracy.