Top Machine Learning Jobs in Columbus, OH

Leads compliance and operational risk management for Consumer and Community Banking. Partners with stakeholders to identify risks, design and monitor controls, conduct risk and control self-assessments, analyze KRI/KPI data, and deliver reporting. Supports regulatory compliance, control program projects, automation, AI, and continuous improvement while advising executives and driving risk mitigation.

Analyze business data, uncover operational improvement opportunities, and develop automation and self-service solutions. The role partners with stakeholders and subject matter experts, conducts ad hoc analysis, delivers actionable insights, and communicates data-driven recommendations to management. It requires exposure to LLMs, machine learning, AWS, Snowflake, APIs, and multiple analytics tools. This is an on-site role requiring five days per week in the office.

Monitor fraud trends in retail deposit acquisition, analyze large datasets, identify defensive gaps, and develop strategies balancing fraud losses, operational costs, and customer impact. Partner with cross-functional and technical product teams to deliver fraud prevention capabilities, present actionable insights to senior leadership, and support business growth while protecting customers.

Analyze large datasets to identify digital fraud trends, develop loss-reduction strategies and business rules, automate fraud alert processes, monitor emerging risks, and present actionable insights to senior leadership. Collaborate with technical and business teams on fraud-fighting products, apply experimentation and machine learning, manage financial impacts, and improve customer experience across digital payment channels.

Manage point-of-sale fraud strategies and conduct complex risk analyses to reduce losses while balancing customer impact. Responsibilities include developing fraud rules and policies, identifying risk indicators, enhancing reporting, testing mitigation processes, leading strategy initiatives end-to-end, analyzing large datasets, and presenting recommendations to management. The role collaborates with Operations, IT, Risk Management, and other cross-functional partners to improve fraud detection and customer experience.

Owns data products supporting customer applications and account opening. Documents and models data, defines metadata and governance requirements, manages data quality and risk, and helps modernize legacy data. Partners with technology, analytics, data science, operations, risk, and business teams to create backlogs, user stories, prototypes, and natural-language querying capabilities. Maintains data documentation and supports delivery of datasets for dashboards, AI/ML, and business users.

Leads software engineering initiatives within an agile team, providing technical direction and delivering secure, scalable production code. Establishes governance for AI-assisted development, code review, testing, troubleshooting, and SDLC automation. Shapes product design, technical operations, engineering standards, and reusable frameworks while serving as a subject-matter expert and coaching senior engineers on responsible AI usage, security, resiliency, and validation practices.

Lead quantitative analytics initiatives by integrating and preparing diverse customer, financial, digital, account, and operational datasets. Analyze customer journeys, identify friction points, optimize collections features, develop KPIs, and apply statistical, machine learning, and predictive modeling techniques. Translate strategic objectives into analytics solutions, collaborate with product and technology teams, and present actionable insights to business and executive stakeholders.

Leads Toast’s AI and analytics strategy for Enterprise Care. Owns the roadmap, evaluates and deploys AI-assisted support tools, develops operational intelligence products, and partners with Product, Engineering, and Data teams to move pilots into production. Establishes complexity and workload intelligence, assesses AI readiness, translates AI performance into measurable operational outcomes, and builds organizational AI literacy. The role requires extensive enterprise support or service-delivery leadership, hands-on production AI experience, operational data fluency, and the ability to drive trusted adoption among agents and executives.

Build and productionize traditional machine learning models, owning feature engineering, validation, deployment, monitoring, drift detection, and retraining. Develop LLM-powered agentic tools, dashboards, self-serve analytics products, and automated insight pipelines. Partner with engineering and business stakeholders, write production-grade Python and SQL, drive adoption, improve data quality, and establish responsible AI workflows for a regulated industry.

Designs and implements scalable AI, machine learning, automation, and generative AI solutions for clients. Responsibilities include assessing AI readiness, developing strategies, leading discovery and solution delivery, integrating AI with enterprise systems, advising on governance and responsible AI, managing client engagements, contributing to reusable AI offerings, and supporting business development. The role requires strong technical, consulting, communication, and stakeholder management skills.

Lead engineer responsible for designing, building, and operating secure, resilient, cloud-native services. Write and review production code, automate remediation and CI/CD pipelines, design event-driven systems with Kafka and Spring Boot, evaluate vendor solutions, mentor engineers, and partner with product stakeholders to deliver scalable technology.

Own the analytics roadmap for a Consumer Banking customer pillar, leading measurement, experimentation, and analyses across growth, retention, pricing, servicing, and strategy. Translate complex business questions into executive-ready recommendations, influence cross-functional decisions, establish analytical standards, and mentor analytics talent. The role requires strong statistical, experimental, communication, visualization, and stakeholder-management skills.

Lead data management for marketing execution: design targeting and measurement strategies, build repeatable datasets, automate reporting, implement monitoring and quality checks, enable responsible AI-supported workflows, and quantify value from automation and audience improvements.

Own and improve Headway’s privacy program across HIPAA, 42 CFR Part 2, state privacy laws, individual rights, incidents, vendor governance, and AI use. Maintain controls, data maps, metrics, training, and operational workflows. Conduct product, vendor, and AI privacy assessments; manage rights requests and incident investigations; coordinate notifications and regulatory submissions; and translate complex requirements into practical guidance for cross-functional teams.

Own the strategy and roadmap for patient-provider search, retrieval, ranking, and marketplace matching. Lead experimentation, objective functions, evaluation, and ML platform foundations while partnering with ML engineers, data scientists, design, clinical teams, and executives. Improve successful, lasting care matches rather than optimizing only clicks or bookings. Balance patient access, provider growth, clinical quality, and payer priorities, while setting the standard for technically grounded product management.

Develop autonomy evaluation metrics, statistical and machine-learning analyses, dashboards, and pipelines for simulation and on-road autonomous vehicle testing. Analyze perception, prediction, and planning performance; identify anomalous behavior and critical scenarios; apply VLMs and LLMs where appropriate; and support release gating and safety decisions. The role requires production Python, C++ debugging, technical leadership, cross-functional collaboration, and rigorous software engineering practices.

Define architecture and metrics for autonomous driving evaluation, lead cross-functional integration across autonomy and simulation teams, develop statistical and machine learning methods, analyze large-scale time-series data, manage evaluation KPIs and release gates, and communicate safety and readiness insights through dashboards and stakeholder presentations.

Serves as the technical bridge between customers, Sales, and technology teams. Leads technical discovery, solution architecture, demonstrations, workshops, proofs of concept, and evaluations using HERE data, APIs, mapping, routing, spatial analytics, cloud, AI, and enterprise integration. Partners with Sales to qualify opportunities, reduce technical risk, develop proposals and reusable presales assets, and guide customers toward location-intelligence solutions.

Lead data science and AI initiatives for Home Lending Operations by developing product analytics, machine learning, experimentation, KPI, and trigger-driven capabilities. Translate ambiguous business problems into actionable insights, improve client journeys and operational efficiency, and identify scalable agentic AI use cases. Partner with product, strategy, operations, and technology teams while communicating recommendations to senior stakeholders in a regulated environment.

Analyze malware, binary files, customer detection tickets, and machine learning detections to improve detection efficacy and reduce false positives. Review product detections, assess binary legitimacy, address internal detection questions, and provide insights to data science teams. The role collaborates with technical account managers, incident response teams, data scientists, and malware researchers. Required capabilities include malware analysis, reverse engineering, Python scripting, binary fundamentals, threat assessment, research methods, operating systems, and AI-assisted workflow improvement.

Lead risk and control activities for Consumer & Community Banking: identify and remediate control gaps, design automated controls and analytics, run RCSA process, produce KRI/KPI reporting, support regulatory compliance, and drive control improvements using automation and data tools.

Lead analytics and machine-learning efforts to prevent point-of-sale fraud: analyze large datasets, design and implement fraud decisioning capabilities, partner with risk modeling and cross-functional teams, build KPI monitoring and automation, and communicate findings and project outcomes to senior leadership.

Lead AI engineering teams to design, build, and deploy end-to-end AI solutions. Manage client relationships, plan and deliver projects, mentor staff, drive adoption of cloud-native microservices, CI/CD, vector databases and orchestration tools, and ensure high standards of quality, innovation, and compliance.

Lead teams applying advanced data science and machine learning to large-scale claims, clinical, and member data to generate actionable insights that reduce cost of care and improve population health. Manage client relationships, mentor staff, ensure project delivery and quality, develop payer-focused analytics products, present findings to senior stakeholders, and promote continuous improvement and knowledge sharing.