Top Machine Learning Jobs in Columbus, OH

Leads financial statement audits for digital assets and financial services clients, evaluating regulatory compliance, governance, risk management, and internal controls. Applies advanced auditing and technical accounting expertise to identify material misstatements and issue audit reports. Directs complex engagements, coaches diverse teams, manages client relationships, communicates findings, and reinforces professional, independence, and ethical standards. The role also involves data analysis and exploring AI and machine learning platforms to improve processes.

Leads compliance and operational risk management for Business Banking by identifying risks, designing and monitoring controls, conducting risk and control assessments, and driving remediation. Partners with stakeholders and executives, analyzes KRI/KPI data, creates reports and presentations, supports regulatory compliance projects, and applies automation, AI, analytics, and agile practices to improve the control environment.

Leads the design and execution of an enhanced control framework for Business Banking. The role oversees risk assessments, control issue detection, escalation, root cause analysis, remediation, anti-fraud and cybersecurity initiatives, and regulatory compliance. It uses analytics, automation, and machine learning to improve controls, while managing change, advising stakeholders, presenting to senior leadership, and leading cross-functional teams across Legal, Compliance, Audit, and Risk.

Owns Small Business Deposits data across sourcing, documentation, delivery, quality, protection, and governance. Partners with product, technology, analytics, operations, risk, controls, and vendors to define data assets, requirements, access controls, retention, and risk mitigation. Leads modernization into enterprise data platforms, resolves delivery issues, aligns stakeholders, and manages direct or matrixed teams delivering data-related outcomes.

Own the analytics lifecycle from complex raw data through executive-ready insights. Design experiments, analyze performance drivers, build scalable data pipelines and analytical frameworks, and communicate findings to marketing, product, and technology stakeholders. The role supports customer acquisition, owned media optimization, repeatable reporting, and roadmap decisions through secure, maintainable SQL and Python or R solutions.

Leads client-facing AI engineering and intelligent automation engagements for health services clients. Manages project planning, budgets, timelines, deliverables, and risks while guiding teams developing machine learning models, algorithms, and cognitive solutions. Reviews Python code and data analysis, validates model performance, coaches team members, identifies automation opportunities, and translates business needs into technical solutions. Promotes responsible AI practices and supports stakeholder collaboration across multiple client workstreams.

Evaluate Business Banking deposit and cash management products using data-driven analysis. Develop pricing strategies, analyze small-business banking behavior, create dashboards and reports, build data assets, automate processes, and provide insights for product development. Collaborate with Product, Design, and Technology teams, participate in senior product reviews, and present recommendations to business leaders.

Leads enterprise email security strategy, architecture, engineering, modernization, and lifecycle management. Oversees phishing protection, threat detection, encryption, DLP, cloud security, incident response, risk remediation, governance, metrics, vendor relationships, and regulatory alignment. Partners with cross-functional stakeholders, presents to executives, leads engineering teams, and applies AI-enabled workflows to improve security analysis and operational effectiveness.

Owns and improves data science platform capabilities that help Data Scientists build, deploy, and operate AI/ML solutions. Responsibilities include representing platform users, shaping product strategy and roadmaps, analyzing usage and adoption metrics, prioritizing backlogs, defining user stories and acceptance criteria, evaluating technologies, partnering with technical teams, and supporting Agile planning and delivery.

Leads the design, development, and deployment of scalable AI, generative AI, and machine learning solutions. Oversees data infrastructure, integration, data quality, and advanced analytics while managing client engagements and executive relationships. Drives business development, strategic direction, innovation, and AI-driven automation. Mentors teams, develops future leaders, and fosters collaboration, integrity, and client satisfaction across complex projects.

Leads the strategy, roadmap, development, and performance of risk tooling products supporting control assurance, audit readiness, risk reporting, and operational risk management. Drives automation of manual risk and controls processes, partners with Engineering, Risk, Audit, Controls, and Architecture teams, establishes governance for complex initiatives, and advances enterprise analytics and AI-enabled risk solutions. Coaches product teams, manages stakeholders, monitors market trends, and ensures product investments deliver measurable business outcomes.

Analyze business data, uncover operational improvement opportunities, and develop automation and self-service solutions. The role partners with stakeholders and subject matter experts, performs ad hoc analysis, communicates actionable insights, and leads simplification projects. It requires experience with analytics and automation tools including Alteryx, Tableau, SAS, SQL, Python, Snowflake, AWS, APIs, and AI/ML concepts. This is an onsite role five days per week and does not offer visa sponsorship.

Supports fixed income middle-office operations from trade capture through settlement and reporting. Responsibilities include exception monitoring, root-cause analysis, trade detail accuracy, portfolio support, process improvement, procedure documentation, offshore process oversight, audit support, and operational risk reporting. The role partners with portfolio managers, fund accounting, custody, performance, reference data, technology, and other stakeholders to improve efficiency, data quality, controls, and platform resiliency.

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.

Leads technical pre-sales for supply chain integration solutions, partnering with Sales to conduct discovery, design ERP/WMS/TMS and trading-partner architectures, run POCs, deliver demonstrations, and respond to RFPs. Advises technical and executive stakeholders on business outcomes and AI opportunities, translates capabilities into measurable ROI, and collaborates with Product, Customer Success, and Professional Services on roadmap feedback and post-sale handoffs.

Lead product strategy, roadmap, and delivery for Falcon Complete’s internal MDR tooling, including AI-powered investigation and triage, ticketing, case management, and analyst workbench systems. Partner with security operations, analysts, engineering, and data science teams to translate workflows into product requirements, prioritize automation, improve analyst productivity and response times, define success metrics, and enhance customer outcomes.

Owns product strategy, roadmaps, prioritization, discovery, and delivery for software, data, and AI products supporting behavioral healthcare. Partners with engineering, clinical, community, operations, and executive teams to translate care models into workflows and product features. Leads user research, experimentation, analytics, and field pilots to improve care delivery and outcomes.

Develop and implement data science strategies for machine learning applications including lead scoring, churn modeling, pricing, personalization, and fraud detection. Own end-to-end initiatives spanning data analysis, feature engineering, model training, inference, and monitoring. Collaborate with engineering, infrastructure, analytics, and business teams to deliver measurable impact, improve data capabilities, and communicate findings to technical and non-technical stakeholders.

Leads data ownership and governance for data products, defining metadata, quality, controls, integration, and risk mitigation. Partners with business, analytics, technology, and engineering teams to deliver curated datasets and production-ready solutions. Uses advanced SQL for validation and root-cause analysis, manages workstreams and staff, and supports analytics and machine learning use cases. Applies AI and LLM tooling to automate governance activities and maintains data integrity across regulated environments.

Leads a data science team within wealth management, delivering analytics, machine learning, experimentation, and AI solutions that improve client outcomes, product performance, and operational efficiency. Partners with product, strategy, and operations leaders to define problems, metrics, and recommendations. Oversees modeling, client behavior analysis, experimentation frameworks, executive communication, and AI use-case development while coaching team members and managing performance.

Leads client-facing generative AI and data modernization engagements, directing solution design, delivery planning, architecture, and technical decisions. Guides teams in prompt engineering, context engineering, agent orchestration, LLM evaluation, RAG, vector databases, and AI guardrails. Oversees cloud AI implementations using Gemini, Vertex AI, AWS Bedrock, or Azure AI Services; develops Python prototypes; evaluates model performance; manages workstreams and deliverables; and mentors staff on AI methods, agile delivery, and technical problem-solving.

Leads engineering teams delivering scalable AI and machine learning solutions on Google Cloud. Oversees generative AI architecture, Vertex AI, LangChain, LangGraph, LLM APIs, RAG, experimentation, evaluation, guardrails, and LLMOps. Guides technical strategy, client alignment, delivery execution, and team development while applying Python, data science, and machine learning methods to build reliable AI products.

Leads Google Cloud AI and machine learning engineering teams and client workstreams. Oversees generative AI, LLM application, agent orchestration, retrieval-augmented generation, model evaluation, prompt engineering, guardrails, and LLMOps initiatives. Reviews architecture, code quality, testing, and solution delivery using Vertex AI, LangChain, LangGraph, Python, and LLM APIs. Coaches team members, improves reusable delivery assets, and manages client relationships, timelines, quality, and expectations.

Leads client-focused generative AI and data modernization architecture, translating business needs into scalable solutions using Google Cloud, Vertex AI, LLMs, vector databases, and modern data platforms. Advises stakeholders, develops AI evaluation and guardrail practices, guides experimentation and deployment, presents technical recommendations, supports proposals, and coaches teams through complex transformation programs.

Leads People Analytics enablement and BI strategy, overseeing semantic models, certified data products, metric governance, data foundations, advanced analytics enablement, demand prioritization, and adoption. Partners with People Analytics, IT, and HR Strategy teams to create trusted workforce data, reusable semantic assets, and decision-ready insights. Manages teams and operating models while driving data quality, governance, certification, self-service analytics, AI access, and scalable insight delivery across the enterprise.