Top Senior Machine Learning Jobs in Columbus, OH

Leads a data science team focused on customer intelligence and personalization across digital products, apps, newsletters, and marketing channels. Develops lifetime value, segmentation, retention, recommendation, attribution, forecasting, and engagement models. Owns product funnel analytics and experimentation, including A/B testing and causal inference. Partners with Product, Marketing, Editorial, Data Operations, and Engineering to translate customer behavior into product improvements, measurable growth, and scalable data capabilities.

Lead design-driven technical solutions that blend UX and software development. Create service flows, wireframes, prototypes, and CX strategies; guide technical teams and integrate user research, AI, and accessibility into products.

Analyze and characterize conversational audio data, identify underrepresented areas, and build active-learning and human-in-the-loop workflows. Develop representative benchmarks, automate model adaptation, and create reusable data analysis and scoring systems. Partner with research, engineering, and data teams to run experiments, improve models, organize data, and translate complex findings into measurable product improvements.

Lead the In-Vehicle Experience team at Samsara, focusing on transforming in-cab cameras into intelligent companions. Collaborate with engineering, prioritize features, and ensure client interactions highlight trust and connection. Conduct customer research and integrate AI and ML into product features.

Lead independent validation and ongoing monitoring of complex financial and analytics models, including ALM, loss forecasting, loan transition, corporate finance, and engagement models. Identify weaknesses, communicate findings, guide remediation, and strengthen model governance. Collaborate with Finance, Quantitative Research, Growth Analytics, Internal Audit, Internal Controls, Accounting, and Compliance to maintain Affirm’s model risk framework and address audit, regulatory, and examiner requests.

Design, build, productionize, and operate supervised and unsupervised ML models, LLM solutions, risk features, and Databricks data pipelines for financial crime monitoring. Develop AI feedback and accuracy measurement, monitor model drift, bias, and performance, and support enterprise-grade ML applications in production. Collaborate with product, engineering, SRE, data science, compliance, and operations teams using agile processes.

Partner with healthcare clients to design, prototype, and deploy production-grade AI/LLM solutions. Lead discovery, architecture, cloud integration, and delivery while troubleshooting distributed systems and driving measurable client outcomes.

Own Secret Detection and Vulnerability Research as a product: define strategy across prevention, detection, validation, revocation, and reporting; ship detection content and intelligence with measurable quality; drive adoption, retention, and revenue; partner with engineering, research, and Field; use AI to accelerate research and productization.

Lead teams building computer vision and machine learning for AI dash cameras, managing model lifecycle, coordinating with cross-functional teams, and driving AI product development.

Provide legal and regulatory monitoring and analysis for Consumer & Community Banking; draft summaries; manage regulatory and change-management matters; design and improve legal processes; evaluate and apply emerging technologies and AI prompts for legal workflows; respond to regulator, audit, and compliance inquiries; support special research projects and collaborate across teams to solve problems and prioritize work in a fast-paced environment.

Designs end-to-end, cloud-first solutions for Small Business digital experiences, spanning web, mobile, APIs, microservices, data, and infrastructure. The role develops AI-enabled architectures, modernizes platforms, applies identity and zero-trust patterns, evaluates emerging technologies, and ensures reliability, security, scalability, and cost effectiveness. It also guides architecture governance, roadmaps, stakeholder decisions, and technical teams.

Lead client AI transformation engagements from discovery through implementation. Develop AI strategies and roadmaps, apply product management practices to prioritize and deliver solutions, collaborate with engineering and data teams, participate in Agile delivery, mentor consultants, support business development, and ensure successful, timely delivery of AI and analytics solutions.

Serve as the Data Product Owner and business advisor for Programs and E-Risk: translate business needs into data requirements, prioritize backlog, coordinate UAT, ensure data quality and governance, collaborate with engineering and stakeholders to deliver trusted data products, reporting, and visualizations.

Lead design, build, and deploy scalable, reliable Java-based microservices in a cloud-native, event-driven AWS environment. Own backend feature design, code quality, automated testing, and FedRAMP release processes. Mentor engineers, participate in on-call/TechOps, integrate AI tools into development, and drive architecture, observability, and cross-team initiatives.

As a Customer Success Engineer, you'll drive adoption and satisfaction for enterprise customers using Deepgram's voice AI technology, fostering strong relationships and aligning technical solutions with business objectives.

Use ML, LLMs, and GenAI to detect and prevent card fraud by analyzing large transaction datasets, building automation and reporting, developing POCs, and delivering actionable recommendations to cross-functional partners to optimize risk and customer experience.

Designs and maintains enterprise data architecture, models, databases, integrations, cloud platforms, governance frameworks, and AI-ready data solutions. Partners with engineers and business stakeholders to translate requirements into scalable technical designs. Establishes standards for security, reliability, performance, data quality, lineage, disaster recovery, and compliance. Supports reporting, business intelligence, analytics, machine learning, and enterprise data modernization initiatives using Microsoft Fabric, Azure, SQL Server, Azure SQL, Data Factory, warehouses, lakes, and lakehouses.

Lead and manage the underwriting and credit risk model validation team, perform hands-on independent validations of complex ML models, assess data integrity and performance, identify and remediate model weaknesses, partner on MRM framework improvements, and coordinate with audit, compliance, and controls to meet regulatory standards.

Lead the development of machine learning systems for Cedar Pay's Personalization Engine, manage ML engineers, and ensure ML model effectiveness.

Lead and deliver secure, high-quality full-stack services: design, develop, test, deploy, and operate production workloads. Drive architecture decisions, reliability improvements, automation/CI-CD, and mentor engineers while partnering with product and stakeholders. Evaluate vendors/solutions and promote engineering best practices across teams.

Leads applied machine learning and data science strategy, roadmap, and delivery. Manages and develops data science talent while overseeing statistical, AI, and machine learning model development. Partners with senior business stakeholders to frame complex problems, deliver scalable solutions, communicate tradeoffs, and drive measurable outcomes. The role includes hands-on technical leadership, model robustness oversight, production deployment and monitoring, and cross-functional influence across enterprise functions.

The Staff Machine Learning Engineer will lead ML initiatives, develop ML systems, collaborate with cross-functional teams, and mentor junior engineers to optimize healthcare intake workflows using advanced machine learning techniques.

Lead and transform the Fraud Intelligence Team by designing scalable operating models, QA and measurement frameworks, and talent programs. Drive labeling quality, operational metrics, and fraud prevention impact; convert frontline fraud observations into insights for Product, R&D, Analytics, and GTM; leverage AI, automation, and tooling; and serve as a cross-functional fraud subject-matter expert while developing early-career talent and measurable business outcomes.

Lead design and deliver AI-native, cloud-native software components; develop secure high-quality production code; drive adoption of AI-assisted engineering practices, CI/CD automation, SDKs and reusable frameworks; mentor engineers and lead cross-team communities of practice.

Senior-level engineer delivering analytics and ML-Ops solutions for healthcare payers. Design and implement analytics platforms on Databricks, develop CI/CD and provisioning patterns, collaborate with data scientists, mentor juniors, and build client relationships to drive actionable insights and operational improvements.