Top Senior Machine Learning Jobs in Columbus, OH

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.

Define and own the technical vision, product strategy, roadmap, data strategy, and AI integration for CrowdStrike’s Next-Gen SIEM and log management products. Translate customer, market, and threat intelligence into shipped capabilities; lead cross-platform integration across endpoint, cloud, identity, and exposure products; and articulate the platform consolidation narrative. Engage executives, customers, analysts, sales, engineering, and product teams while driving detection, investigation, response automation, and cybersecurity thought leadership.

Build and maintain core infrastructure for developing, training, evaluating, deploying, and operating machine learning models and pipelines for Atlassian’s GenAI products. Solve complex software infrastructure and architecture challenges, lead engineers through technical projects from design to launch, and collaborate with internal teams and customers. The role involves large-scale distributed systems, data processing, cloud platforms, CI/CD, and machine learning lifecycle support.

Designs scalable data architectures, ELT/ETL pipelines, data models, quality frameworks, and self-service data platforms. Partners with engineering, product, program, leadership, and data science teams to translate business needs into technical solutions. The role includes end-to-end technical ownership, large-scale batch and streaming data processing, mentoring junior engineers, system optimization, and on-call support for platform stability.

Own the modeling and statistical lifecycle for enterprise AI initiatives. Build predictive models for anomaly detection, workflow forecasting, and risk mitigation; analyze and wrangle large-scale financial data; validate accuracy and bias; ensure regulatory compliance; create training datasets; establish model performance KPIs; and communicate quantitative findings to business and technology stakeholders.

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.

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.

Develops advanced thermal controls algorithms for optimization and condition-based maintenance. Responsibilities include requirements analysis, algorithm prototyping, data analysis, control design, production-code integration, simulation modeling, automated testing, documentation, and field-test support across Vertiv’s global thermal product portfolio.

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.

Build and deploy production AI/ML systems that optimize Samsara’s global hardware supply chain. Responsibilities include demand, inventory, cost, lead-time, supplier-risk, anomaly-detection, and cash-flow modeling; real-time inference pipelines; MLOps; data infrastructure; governance; and cross-functional AI adoption. The role also sets the supply-chain AI roadmap, mentors junior scientists, establishes modeling standards, and partners with Planning, Procurement, Finance, Engineering, and Operations.

Build and scale production machine learning solutions for lottery fulfillment. Develop and optimize computer vision and multimodal models, fine-tune and evaluate models, create data pipelines and evaluation frameworks, and deploy models on cloud infrastructure. Integrate models into real-time systems involving image processing, barcode decoding, and OCR. Collaborate cross-functionally, improve engineering standards, mentor teammates, and support reliable nationwide expansion.

Leads enterprise AI and software architecture, designing scalable AI-driven platforms and deploying machine learning solutions in cloud environments. Defines technical strategy, selects technologies, establishes MLOps, DevOps, testing, and engineering practices, and ensures privacy, security, and ethical compliance. Collaborates with business, product, and engineering teams, mentors technical staff, and drives innovation, long-term planning, and delivery excellence.

Supports email and broader marketing analytics through data collection, campaign measurement, reporting, dashboards, statistical analysis, visualization, and optimization recommendations. Partners with marketing teams to translate customer and campaign data into actionable insights. Responsibilities include developing analytical models, conducting qualitative and quantitative research, documenting requirements, supporting implementation, and improving marketing processes and decision-making.

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.

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 AI enablement across Property and Casualty by identifying high-impact use cases, translating business needs into AI roadmaps, overseeing initiatives from ideation through deployment, and coordinating cross-functional teams. Establishes governance, ethical AI practices, KPIs, change management, workforce planning, and AI literacy programs. Builds stakeholder alignment, develops business cases, assesses data readiness, and promotes scalable adoption of generative and agentic AI capabilities.

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.

Serve as primary client contact for a portfolio of loyalty-program clients. Scope and manage end-to-end actuarial analyses rooted in predictive modeling and machine learning, validate model outputs, translate results into clear recommendations and presentations, and partner with analysts and data scientists to deliver high-quality, client-facing work in a fully remote consulting environment.

Leads multiple software engineering teams responsible for cloud-first data platforms, scalable Java and Python microservices, AI-assisted engineering, and near-real-time stream processing. Oversees architecture, secure production development, CI/CD, resiliency, observability, and modernization on AWS using Kafka, Kinesis, and Flink. Manages 8–10 engineers in a matrix environment, establishes governance for AI-enabled development, mentors technical leaders, and drives measurable improvements in delivery, quality, reliability, and operational efficiency.

Lead growth data science initiatives by partnering with Marketing, Sales, and Customer Experience leaders. Develop analyses, experimentation frameworks, and machine learning models for acquisition, engagement, retention, monetization, targeting, segmentation, uplift modeling, and lifetime value prediction. Productionize reliable models with engineering teams, communicate insights to senior stakeholders, establish experimentation best practices, and mentor junior data scientists. The role requires strong business judgment, advanced technical expertise, and experience operating in ambiguous, zero-to-one environments.

Designs, develops, deploys, and supports complex enterprise data pipelines, data models, ETL/ELT processes, and analytical solutions. Leads data governance engineering initiatives involving metadata catalogs, lineage, data quality, and remediation. Builds governed, AI-ready datasets and integrates AI capabilities into production data workflows while validating performance, security, privacy, and reliability. Collaborates with business, technology, data science, and application teams to deliver scalable, compliant solutions.

Build and deploy production machine learning systems for personalized recommendations, fraud detection, risk scoring, ranking, classification, and anomaly detection. Own the full ML lifecycle, including mathematical formulation, feature engineering, training, evaluation, A/B testing, deployment, monitoring, and reliability. Develop low-latency real-time inference services using large-scale donor and transaction data, collaborate with Platform and DevOps teams, and mentor engineers on ML best practices and scientific rigor.