Top Machine Learning Jobs in Columbus, OH

Lead machine learning and data science initiatives supporting workforce planning, including demand forecasting, capacity planning, resource scheduling, and business analysis. Design, develop, validate, and document statistical, AI, and machine learning models; lead complex projects; mentor junior team members; partner with stakeholders; and communicate data-driven recommendations. The role requires advanced quantitative expertise, model governance experience, and proficiency with data science programming and big-data technologies.

Develops financial data mining, analytics, reporting, AI tools, and scalable cloud applications. Aggregates data sources, improves repositories and processing workflows, ensures data accuracy and security, and partners with finance and MIS teams on strategic insights. Leads model training and deployment, supports business reporting, provides technical guidance, and recommends performance improvements.

Leads AI infrastructure engineering strategy, roadmaps, governance, and delivery across Consumer & Community Banking. Builds and manages high-performing engineering teams, oversees budgets and vendors, and partners with senior stakeholders on AI platform requirements, risk, security, compliance, and reliability. Directs production ML and GenAI workloads using cloud infrastructure, Kubernetes, GPU orchestration, infrastructure as code, automation, observability, model serving, and AI gateways. Drives large initiatives, operational excellence, cost optimization, and auditable AI-assisted engineering practices.

Leads cross-functional initiatives that apply artificial intelligence, machine learning, and analytics to improve branch coaching, sales execution, and operational outcomes. Responsibilities include defining roadmaps, success metrics, and measurement plans; coordinating product, engineering, data science, and business teams; managing risks and dependencies; ensuring responsible AI governance; and driving change management and adoption.

Investigate complex AML cases, review transaction-monitoring alerts, determine SAR/STR filing decisions, and communicate findings to regulators, law enforcement, and stakeholders. The role also supports audits, improves AML detection models and typology coverage, analyzes transactional data, identifies financial crime trends, and provides analyst training. Candidates need strong blockchain, AML regulations, investigative writing, and blockchain forensic tool experience, with AI and SQL capabilities preferred or advantageous.

Lead data architecture across more than 100 applications, establishing architecture principles, database standards, data modeling practices, governance frameworks, and data exchange patterns. The role drives migration to strategic cloud data lakes, reduces duplicate data platforms, defines target-state architectures, serves as a subject matter expert, and mentors junior architects. It requires extensive experience with cloud databases, data lakes, data governance, data quality, analytics, machine learning, and distributed enterprise technology environments.

Leads data science for medical and workers’ compensation claims, identifying AI/ML opportunities and delivering machine learning, GenAI, LLM, RAG, and agentic solutions. Partners with Claims, Technology, Product, Engineering, and UX teams to establish roadmaps, build intelligent systems, measure business impact and quality, implement MLOps practices, and mentor stakeholders. The role begins as a senior individual contributor and is expected to build a team.

Leads data science strategy and execution for medical and workers’ compensation claims. Builds machine learning, generative AI, LLM, RAG, and agentic solutions; establishes roadmaps, evaluation metrics, monitoring, and operational practices; partners with claims, engineering, product, architecture, UX, and technology teams; and provides technical mentorship. Initially an individual contributor, the role is expected to build a team over time.

Owns strategic planning, governance, prioritization, risk management, and execution across a portfolio of Superintelligence Lab programs. The role establishes phase-gate standards, manages change, tracks portfolio health through dashboards and metrics, resolves blockers, advises cross-functional teams, and communicates progress to executives. It requires strong technical program management experience, Jira proficiency, Agile and SDLC knowledge, and the ability to apply AI technologies to improve business outcomes.

Leads an analytics team supporting Home Lending Operations by framing business problems, delivering analysis, building reporting, defining data requirements, and advising senior stakeholders. Develops AI and machine learning use cases to optimize operations and customer experiences, establishes analytics quality standards, supports experimentation and product measurement, and partners cross-functionally with Operations, Product, Technology, Finance, Strategy, Business Intelligence, and Modeling teams.

Build and deploy production-grade AI and machine learning solutions for marketing and sales, including LLM agents, data-quality pipelines, semantic search, RAG, analytics, and integrations with Salesforce, Marketo, and Adobe platforms. Define evaluation and governance practices, monitor AI services, implement privacy controls, and collaborate with platform teams. Train and mentor technical and business stakeholders on responsible AI adoption while supporting DevOps, incident response, and operational runbooks.

Leads a multi-team organization building AI-driven cybersecurity systems for endpoint and cloud environments. Owns technical strategy, roadmap, hiring, delivery, evaluation, and safe production deployment of AI models and autonomous defense agents. Oversees systems operating under strict endpoint resource limits and at cloud scale across trillions of events. Partners with engineering, product, threat intelligence, and executives while advancing security efficacy, reliability, and responsible AI adoption.

Leads finance data product discovery, requirements, stakeholder alignment, backlog management, and delivery readiness from concept through release. Translates business needs into data requirements, epics, features, user stories, prototypes, and acceptance criteria. Coordinates cross-functional technology, data, product, and finance teams while managing Jira and Jira Align roadmaps, dependencies, risks, and decisions. Identifies opportunities to apply artificial intelligence, machine learning, natural language querying, and automation to improve business decisions.

Leads quantitative analytics initiatives that transform complex data into actionable insights and scalable, governed solutions. Responsibilities include stakeholder partnership, self-service analytics development, customer journey analysis, AI and automation application, executive presentations, production reporting support, regulatory compliance, process improvement, and mentoring junior analysts.

Leads AI transformation, governance, quality oversight, risk management, and responsible adoption across Pfizer Global Supply and QSEO. Establishes GxP-compliant AI lifecycle practices, inspection-readiness frameworks, governance forums, portfolio monitoring, inventory management, metrics, and reporting. Advises senior leaders on AI regulations, deployment risks, investment priorities, and compliance while leading cross-functional initiatives and influencing stakeholders across Quality, Digital, Operations, and R&D.

Leads the development and deployment of data solutions using Palantir Foundry and AIP. Manages engineering teams, coaches team members, oversees project planning and execution, analyzes system interactions, manages client relationships, and drives innovation. The role applies software engineering, data engineering, analytics, and AI/ML techniques to deliver scalable client solutions while maintaining technical and professional standards.

Leads the development and deployment of software and data solutions using Palantir Foundry and AI/ML technologies. Responsibilities include data engineering, analytics, system improvement, client account management, project ownership, team leadership, coaching, stakeholder engagement, and continuous improvement. The role requires building scalable applications with large datasets and delivering customer-focused solutions.

Own and grow strategic enterprise customer relationships, driving retention, renewals, expansion, and seven-figure software transactions. Develop multi-year account plans, build executive and technical stakeholder relationships, identify expansion opportunities, lead negotiations, maintain forecasts and pipeline visibility, and coordinate cross-functional teams. Engage with data scientists and AI/ML engineers to understand adoption, customer outcomes, and opportunities to expand platform usage across business units and use cases.

Leads Corporate Responsibility data strategy, governance, ownership, integration, protection, quality, and risk management. Partners with business, technology, analytics, risk, legal, compliance, and data teams to deliver trusted data assets and platforms supporting operations, reporting, advanced analytics, machine learning, and responsible AI. Establishes accountability, prioritizes remediation, reports governance metrics to senior leadership, and manages direct and matrixed teams.

Own the strategic vision and roadmap for Coursera’s AI-driven search experience. Lead conversational search, personalization, semantic and multilingual search, ranking, and discovery improvements. Partner with engineering, design, research, data science, ML, marketing, and partnerships teams to deliver scalable platform capabilities. Use experimentation, market analysis, KPIs, and customer insights to improve conversion, merchandising, learner experience, and revenue.

Leads GitLab’s Product Data Science team, setting its strategy, roadmap, analytical standards, and priorities. Partners with Product and Engineering leaders on experimentation, causal inference, analytics, and production modeling. Manages, hires, coaches, and retains data scientists while building a high-performing function. Provides hands-on technical review of models, analyses, and queries, and communicates insights and risks to senior leaders.

The Data Scientist will partner with Sales, Revenue Operations, and Finance to analyze GTM performance, build and productionize machine learning models, conduct experiments, forecast outcomes, and identify growth opportunities. Responsibilities include statistical analysis, causal inference, Python and SQL development, MLOps, agentic workflow automation, semantic-layer contributions, executive reporting, and communicating insights to nontechnical stakeholders in a fully remote environment.

Deploys, configures, optimizes, and supports CrowdStrike Falcon’s AI-powered detection and response capabilities. The role tunes machine learning models, develops security automation playbooks, troubleshoots customer environments, integrates Falcon with existing tools, supports threat hunting and forensic analysis, and develops AI training and customer-facing materials. Requires extensive IT deployment, endpoint, security automation, operating system, AI/ML, scripting, and customer consulting experience, with travel up to 25%.

Leads technology risk and control activities across an application portfolio. Responsibilities include identifying and mitigating operational and compliance risks, evaluating control effectiveness, managing issues, conducting root cause analysis, reporting risk insights, and supporting regulatory compliance. The role also applies enterprise AI and machine learning governance, validates AI-assisted outputs, protects sensitive data, and advises technology leaders and senior management on risk posture and remediation strategies.

Leads SailPoint’s global corporate communications function, including corporate narrative, media relations, PR campaigns, crisis communications, executive coaching, podcast strategy, partner communications, and measurement. The role manages internal and agency teams, translates complex cybersecurity and AI concepts into compelling stories, supports product launches and company milestones, builds rapid-response capabilities, and advises executives on reputation and communications strategy.