Top Senior Machine Learning Jobs in Columbus, OH

Manages compliance and operational risk controls for Business Banking Lending. Partners with stakeholders to identify risks, assess control gaps, design and monitor automated controls, lead risk and control self-assessments, manage issues and action plans, and support regulatory compliance. Uses data analytics, visualization, automation, and process improvement methods to strengthen control effectiveness, reporting, and risk mitigation.

Analyze customer behavior across digital and phone journeys, develop experiments and OKRs, and translate data into actionable recommendations. Partner with product, technology, design, modeling, and analytics teams to improve customer experiences and operational efficiency. Present insights to senior stakeholders, support predictive analytics and machine learning initiatives, and maintain data governance through collaboration with data owners.

Leads complex data engineering initiatives supporting global cybersecurity analytics. Designs data architectures, ingestion pipelines, governance standards, and quality processes; collects, transforms, and models traditional and big data; develops Power BI visualizations; and improves data reliability and accessibility. Provides technical consultation, mentors team members, documents systems, presents insights, and responsibly applies AI tools. Requires strong Python, Pandas, SQL, cloud, Snowflake, Airflow, ETL, API, data modeling, and governance expertise.

Leads cybersecurity engineering teams developing enterprise security solutions across multiple technology domains. Clarifies security requirements, supports multi-level network security, performs threat modeling and vulnerability assessments, and advises leaders during security risks. Uses and validates enterprise-authorized AI for risk analysis, documentation, security testing, and control validation while ensuring data sensitivity, auditability, and resiliency. Coaches, develops, hires, and recognizes engineering talent.

Leads applied AI and machine learning initiatives, architecting scalable production systems using LLMs, AI agents, RAG, real-time predictions, and big-data processing. Provides technical direction, develops and reviews secure code, guides model and platform quality, and advises executives on ML strategy. Responsibilities include MLOps, model monitoring, data-quality improvements, production feedback loops, and building reliable solutions that improve operational efficiency and customer 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.

Develop and implement AI solutions and machine learning systems that enhance products and solve complex client problems. Responsibilities include advanced analytics, LLM production deployment, prompt engineering, RAG pipeline optimization, cross-functional collaboration, technical discovery, responsible AI advocacy, client relationship building, and mentoring junior team members.

Manage compliance and operational risk controls within Consumer and Community Banking. Partner with stakeholders to identify risks, assess control gaps, design and monitor controls, lead risk and control self-assessments, and support regulatory compliance initiatives. Use automation, AI, analytics, and visualization tools to improve control effectiveness and reporting. Lead projects, communicate findings, advise executives, and drive technology-enabled process improvements.

Leads pricing and revenue data science initiatives for consumer markets clients. Develops predictive models, forecasts trends and customer behavior, manages dashboards and visualizations, ensures data quality, and translates complex analytics into strategic decisions. Oversees client engagements, validates outcomes, directs complex projects, and coaches diverse, high-performing teams.

Leads a strategic insights team analyzing product development productivity, operating models, workflows, and AI adoption. Frames hypotheses, conducts quantitative analysis and experiments, evaluates delivery and business outcomes, and translates findings into executive recommendations. Partners with Product Operations, Technology, Product, and Finance to implement actions, track adoption, and measure results. Establishes analytical standards, communicates with senior leaders, and develops a high-performing analytics team.

Lead go-to-market strategy for Sprout Social’s AI portfolio, including the Trellis AI agent. Develop enterprise launch playbooks, positioning, executive messaging, campaigns, and enablement programs. Analyze market and competitive dynamics, influence product roadmaps, improve launch processes, and partner with Product, Sales, Revenue, Brand, and executive leadership to drive adoption, revenue growth, deal expansion, and category leadership. The role also mentors marketing team members and operates with substantial autonomy.

Manage deposit-account fraud losses through advanced analytics, reporting, data mining, and machine learning. Identify fraud trends and mitigation opportunities, improve predictive models, develop dashboards, communicate insights to leadership, and support technical projects. Collaborate with banking, risk, operations, IT, vendors, and consultants while mentoring teams and driving analytical best practices.

Lead cybersecurity strategy and architecture across products, applications, and technologies. Advise engineering teams, communicate complex risks to leadership, develop reusable security frameworks, create mitigation solutions, and evaluate emerging technologies. The role requires influencing cross-functional teams, shaping enterprise-wide architecture, and applying cloud-native, application, API, web, and mobile security expertise across a complex financial-services environment.

Leads software engineering and AI initiatives for enterprise-scale banking technology. Designs scalable frameworks, writes and reviews production code, and establishes secure, auditable agentic AI development workflows. Defines responsible AI guardrails, target-state architecture, reusable engineering practices, and cloud-native solutions. Advises cross-functional teams, influences senior stakeholders, serves as a technical subject matter expert, and collaborates on product and engineering roadmaps.

Lead agile design and development projects for mobile apps and digital products. Coordinate product, design, engineering, marketing, leadership, and client stakeholders; manage project plans, scope, requirements, backlogs, acceptance criteria, deadlines, workflows, and deliverables. Support scoping and client relationship management while ensuring successful end-to-end software launches and alignment with business objectives.

Leads software engineering for a Machine Learning I/O team, developing secure production code and guiding design, testing, debugging, and operational stability. Drives enterprise adoption and governance of AI-assisted development tools, establishes validation standards for AI outputs, promotes secure and responsible AI practices, and influences technical decisions across teams. Provides technical direction, serves as a subject matter expert, and advocates for scalable cloud-native engineering practices.

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.

Develop and operate production ML and AI systems powering patient experiences such as matching, ranking, recommendations, onboarding, and personalization. Build reusable ML infrastructure, pipelines, evaluation, observability, and model-serving capabilities. Provide technical leadership across the ML lifecycle, guide architecture, evaluate custom versus foundation-model approaches, and partner with ML and Patient Engineering teams to deliver reliable, scalable AI products.

Leads the AI Engineering team across applied AI features and foundational ML infrastructure. Responsibilities include managing Senior and Staff engineers, hiring and coaching, deploying LLM-powered capabilities, building MLOps and AI observability platforms, balancing product delivery with platform investment, and ensuring privacy, compliance, evaluation rigor, and clinical safety in production systems.

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.

Set the technical vision for data science, lead complex statistical and machine learning model development from concept through production, and architect scalable modeling platforms and pipelines. Develop credit risk, pricing, and loss forecasting models; establish model governance standards; partner with senior leaders; mentor data scientists; and communicate technical strategy to executives. The role emphasizes production deployment, monitoring, cloud architecture, and high-impact decision-making across the business.

Leads control management for JPMorganChase’s Consumer and Community Banking Operations portfolio. Designs and strengthens risk and control frameworks, identifies emerging compliance and operational risks, drives governance and issue management, and promotes consistent agile control practices. Partners with Product, Technology, Legal, Compliance, Audit, and Risk stakeholders to support transformation initiatives, regulatory compliance, control effectiveness, and operational resilience. Requires strong risk-based decision-making, change management, stakeholder influence, executive communication, and data literacy.

Leads technology risk and controls management within Identity and Access Management, including risk assessment, control governance, regulatory compliance, issue remediation, and executive reporting. Develops and matures Continuous Control Monitoring capabilities using automation, data analytics, AI, and machine learning to automate testing, detect exceptions, identify emerging risks, and improve control effectiveness. Partners with technology, data, product, business control, and regulatory stakeholders to strengthen the control environment.

Develop and maintain production machine learning models, build scalable training and serving technologies, optimize real-time and batch ML pipelines, and monitor model performance and drift. The role involves deep neural networks, recommendation systems, automated retraining, ML research, engineering excellence, and communicating technical concepts to diverse audiences. Candidates need 6+ years of industry ML experience, strong coding skills, and experience deploying neural networks at scale.

Own the strategy, roadmap, and delivery of Applicant Security products addressing identity fraud, resume fraud, bot activity, and abuse. Partner with engineering, data science, cybersecurity, legal, HR, and vendors to define requirements, prioritize backlogs, manage releases, establish success metrics, and ensure privacy and compliance. Lead third-party security vendor evaluation and governance while balancing risk reduction with candidate experience in a complex enterprise environment.