Top Machine Learning Jobs in Columbus, OH

Label and annotate call transcripts and datasets to support ML models; validate AI/ML outputs; create taxonomies; analyze call data using GenAI tools; identify anomalies; collaborate with data scientists, engineers, and stakeholders to improve annotation processes and model performance.

Lead control and risk management activities within Consumer & Community Banking. Partner with stakeholders to identify gaps, design and implement control strategies, use automation/AI/data analytics for monitoring, own Risk & Control Self-Assessments, drive regulatory compliance projects, and recommend improvements using digital risk management and agile practices.

Lead market risk oversight for Real Estate funds in Alternatives: measure and analyze market, liquidity, performance and counterparty risks; implement and improve risk frameworks, controls, and reporting; conduct deep dives on emerging risks; collaborate with investment and control teams and escalate issues to senior management.

Develop, troubleshoot, and maintain secure, high-quality code for data and analytics products. Work across the SDLC on batch and streaming data pipelines, data warehousing, and cloud solutions (AWS/Azure/GCP), using Java, Python/Scala, Spark, SQL and enterprise AI-assisted development tools while following security and resiliency practices.

Designs, develops, debugs, and maintains secure, high-quality production software. Produces architecture and design artifacts, analyzes large datasets, and improves system resiliency. Contributes to engineering communities and supports agile CI/CD practices while incorporating cloud and AI/ML capabilities.

Lead Oracle Cloud Finance transformation projects, design Oracle ERP/EPM solutions, improve financial reporting with Oracle Financials/Hyperion, integrate RPA and analytics, coach teams, ensure regulatory compliance, and manage stakeholder relationships across cross-border engagements.

Lead teams to apply advanced data science and machine learning to large-scale claims, clinical, and member data to generate actionable insights that reduce cost of care, improve population health, develop analytics products, and manage client relationships.

Lead strategy and a portfolio of initiatives to drive advisor conversion and productivity using data, experimentation, and AI. Prioritize opportunities, define success metrics, translate needs into analytics/technology requirements, drive cross-functional delivery, and measure post-launch impact.

Lead AI-enabled process and technology transformation for card lending operations: identify and prioritize automation opportunities, define future-state processes, translate needs into requirements and user stories, partner with Technology for delivery and UAT, quantify value with data and financial analysis, drive stakeholder alignment and change management, and ensure governance and measurable benefits realization.

Lead cybersecurity architecture for cloud-native enterprise solutions: evaluate controls and new technologies, advise stakeholders, provide technical guidance, promote firmwide SDLC practices, and mentor peers to mitigate risk and adopt strategic solutions.

Nine-week summer analyst program within Operations supporting processing, trade/asset support, reporting, transformation, and data management. Responsibilities include transaction processing, reconciliations, client lifecycle support, data analytics and reporting, risk reporting, process improvement, and creating Tableau dashboards. Program includes training, networking, and may lead to full-time roles.

Lead adoption of agentic AI across product teams by coaching, prototyping, and embedding new AI-enabled workflows. Redesign operating models, translate AI concepts into playbooks and learning paths, measure adoption and impact, and partner with leaders to scale responsible AI practices across product delivery in a large regulated banking environment.

Lead risk and control efforts for Business Banking Lending products by identifying gaps, designing controls, leveraging automation/AI and analytics, conducting RCSA walkthroughs, driving remediation projects, and partnering with stakeholders to ensure regulatory compliance and continuous improvement.

Lead strategy, implementation, and scaling of AI-powered coaching agents across the firm. Own end-to-end product lifecycle, benchmark emerging AI tech, collaborate with cross-functional teams, ensure governance/compliance, track performance, and produce technical documentation and roadmaps.

Lead design and build of scalable, secure AI/LLM-based production systems and frameworks; develop reusable tooling, evaluation and quality mechanisms; review code; guide cross-functional teams; design multi-step AI workflows and integrations; present technical recommendations to senior leaders.

Own end-to-end product lifecycle for a global entity data platform: resolve organization records into a trusted "golden profile," drive roadmap and backlog, define success metrics (precision/recall, throughput, completeness), enable explainable ML-assisted matching, manage third-party data onboarding, and partner with engineering, ML, governance, and business teams to deploy production-grade, auditable data products at scale.

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.

Design and implement enterprise cybersecurity architecture for cloud-native applications. Embed security across SDLC and CI/CD, build Python automation for monitoring and remediation, evaluate vendors, lead practice communities, and collaborate with risk and compliance to translate regulatory requirements into technical controls while validating AI-assisted security outputs.

Lead architecture solutions across software applications and platforms. Provide technical governance, evaluate and adopt technologies, guide teams and vendors, develop and review secure production code, and influence product design and operational practices. Act as subject-matter expert and advocate firmwide frameworks, tools, and SDLC best practices.

Apply advanced quantitative and computational methods to build and deploy machine learning, optimization, and statistical models on large structured and unstructured consumer data. Drive full modeling lifecycle from problem definition to communication of results and recommendations, present findings to senior stakeholders, and act as a subject-matter expert while contributing new ideas to the team.

Build and validate ML, optimization, and statistical models on large structured and unstructured consumer datasets; generate insights and actionable recommendations; communicate results to senior stakeholders; contribute across the full modeling lifecycle and advance team knowledge.

Lead cybersecurity architecture for cloud-native applications within consumer banking: define target state, assess risks, propose mitigations, drive automation, lead vendor evaluations and communities of practice, and communicate solutions to senior stakeholders.

Design, validate, and monitor card fraud strategies using data analysis and analytics/ML. Partner with product, technology, and operations to develop, test, and implement fraud detection and treatment capabilities while balancing loss, costs, and customer impact.

Lead product strategy and end-to-end lifecycle for outsourcing due diligence offerings. Drive discovery, roadmap, backlog, requirements, and launches; collaborate with engineering, design, and stakeholders; track metrics, gather customer feedback, and integrate AI/LLM capabilities to deliver scalable, reliable products.

Lead control design and monitoring across Consumer & Community Banking by identifying and mitigating compliance and operational risks, implementing automation and analytics, owning risk and control self-assessments, advising stakeholders, and driving control improvements and regulatory compliance.