Top Senior Machine Learning Jobs in Columbus, OH

Leads business analytics engagements, translating stakeholder needs into data requirements, models, dashboards, forecasts, statistical analyses, and actionable recommendations. Performs data exploration, querying, modeling, and visualization across large datasets; supports data governance, quality, security, and stewardship. Mentors analysts, communicates insights to leaders, collaborates with enterprise data teams, and identifies opportunities to apply machine learning in business decision-making.

Develop and deploy UiPath-based RPA solutions for internal and external clients. Responsibilities include requirements gathering, process documentation, automation development, testing, deployment, queue management, scheduling, exception handling, debugging, governance, and Center of Excellence support. The role also involves business process analysis, system design, process optimization, change management, tool selection, and collaboration with developers, analysts, testers, and consulting teams.

Leads OuterBox’s internal AI strategy, AI engineering team, and development of client-facing AI services. Identifies market opportunities, converts internal capabilities into priced consulting offerings, oversees production deployments and partner programs, and drives AI adoption, training, and measurement. Partners with executives, clients, engineering, and technology teams on architecture, security, cloud governance, reliability, pricing, and ROI.

Own analytical relationships with travel technology partners, creating scalable reporting on supply, pricing, and performance. Conduct exploratory analysis, apply machine learning and statistical methods, build dashboards, automate recurring workflows with AI tools, and translate complex findings into recommendations for product, engineering, commercial, and executive audiences.

Build and deploy AI-enabled solutions for Marketplace operations and commercial challenges. Identify opportunities, frame ambiguous business problems, develop prototypes using enterprise data and AI/LLM tools, validate business value, and transition successful solutions into production. Partner with business, product, technology, and data teams; drive adoption, measure outcomes, coordinate initiatives, mentor team members, and enhance reusable platforms and frameworks.

Build and deploy machine learning models for risk and fraud detection. Lead technical efforts in the risk and fraud domain, use SQL and Python for data preparation and production code, design statistically sound experiments, evaluate model performance, and partner with Operations to respond to evolving financial threats.

Conducts econometric, causal, statistical, and optimization analyses to improve operational efficiency, resource allocation, product performance, and business outcomes. The role defines efficiency measures, develops forecasting and optimization tools, partners with product and engineering teams on data instrumentation and implementation, and communicates findings to executives through clear reports.

Drive enterprise SaaS sales from prospecting through close, expanding adoption among strategic accounts. Engage business and technical decision-makers, including C-suite stakeholders, to address fraud challenges using Sift’s machine learning platform. Build consultative business cases, articulate platform differentiation, collaborate cross-functionally, and help shape sales strategy and growth. Requires strong organizational skills, adaptability, and experience managing complex B2B sales cycles.

Build and maintain a full-stack sewer inspection analysis platform using React, TypeScript, Node.js, PostgreSQL/PostGIS, AWS, computer vision, geospatial analysis, and Temporal.io. Responsibilities include developing interactive dashboards, scalable APIs, video-processing workflows, mapping and risk-assessment tools, microservices, cloud infrastructure, and automated testing and deployment pipelines.

Leads the team building LTK’s foundational AI platform, including shared models, services, evaluation frameworks, and production LLM capabilities. Responsibilities include setting the AI roadmap, enabling internal engineering and product teams, overseeing deployment and operational performance, managing quality and cost, and establishing evaluation and monitoring practices. The role also involves hiring, coaching, performance management, and developing a high-performing data science and machine learning organization.

Drive enterprise SaaS sales across EMEA by prospecting AI and machine learning stakeholders, conducting discovery, presenting Arize’s platform, managing proof-of-concept engagements, navigating complex sales cycles, closing new business, driving adoption, and expanding customer accounts. The role requires full-cycle enterprise sales expertise, strong communication, independent execution, account research, and relationship-building within the AI/ML community.

Lead actuarial pricing for Lemonade’s car insurance products, including state rating plans, rate indications, predictive modeling, pricing strategy, and rate filing support. Partner with Data Science to integrate machine learning models, monitor pricing and underwriting results, assess competitors, and expand into new states. Manage and develop a team of actuaries while supporting scalable pricing tools and data-driven decision-making.

Leads the global Customer Success organization and owns post-sale strategy, retention, expansion, adoption, customer health, and recurring revenue performance. Builds scalable teams, processes, lifecycle frameworks, and operating systems supporting rapid international growth. Integrates AI, predictive analytics, and automation into Customer Success operations while partnering with Sales, Product, Engineering, Marketing, and Operations. Manages executive relationships with strategic customers, drives forecasting accuracy, and ensures measurable customer outcomes across distributed regions.

Lead complex actuarial pricing work for personal lines insurance, including rate indications, regulatory filings, pricing analyses, predictive-model implementation, and state expansion. Develop automation and scalable filing tools, communicate recommendations to technical and executive stakeholders, manage regulatory risks, and coach other actuaries. Partner cross-functionally with Product, Regulatory, Data Science, Engineering, and Finance teams while maintaining analytical rigor and compliance.

Lead product design for Fetch’s Receipt & Rewards experience, connecting receipt capture, account linking, recognition, matching, and rewards. Define design strategy for complex, AI-enabled systems; improve usability, accessibility, trust, and experience quality; and balance user needs, data quality, business goals, and technical constraints. Partner with Product, Engineering, Research, and Data Science, drive cross-functional alignment, shape multi-quarter initiatives, and mentor designers.

Designs, builds, scales, and operates distributed CI/CD services supporting millions of builds, tests, and releases. Develops code for Azure-based infrastructure spanning virtual machines, containers, and custom lab environments. Responsibilities include requirements analysis, architecture and design documentation, estimation, debugging, optimization, operational playbooks, on-call incident response, monitoring, reliability improvements, and mentoring engineers.

The Commercial Legal Director will lead complex technology and commercial transactions, negotiate SaaS, licensing, partnership, marketing, and services agreements, and develop scalable legal contracting standards. The role advises teams on AI, intellectual property, privacy, security, and regulatory issues; improves contracting workflows using Ironclad and AI-enabled tools; and partners with senior stakeholders across product, engineering, finance, marketing, and people operations.

Own analytics for Root’s Customer Experience domain and policyholder retention. Develop metrics, analytical frameworks, experimentation systems, and strategic recommendations. Partner cross-functionally with Product, Engineering, Data Science, and Operations to shape roadmaps, evaluate customer initiatives, quantify business impact, and influence senior leadership decisions through rigorous analysis and clear communication.

Build and scale Pulse’s technology partner ecosystem by recruiting and signing partners, structuring integrations and commercial agreements, developing joint go-to-market and co-sell programs, and driving partner-sourced pipeline, revenue, and API usage. The role also manages partner onboarding, enablement, deal attribution, forecasting, technical validation, and feedback to product and engineering teams.

Lead and grow a global engineering team building endpoint visibility, prevention, and on-device ML across Windows, macOS, and Linux. Own roadmap and delivery, direct model development and telemetry, drive cross-platform protection parity, coordinate with product and partner teams, and represent the team externally through research and conferences.

Owns the intelligence platform roadmap for enterprise healthcare engagement, including decisioning, orchestration, integrations, interoperability, identity and consent management, and scalable deployment. The role defines strategy and requirements, prioritizes platform investments, leads partner integrations, establishes success metrics, and coordinates Engineering, AI/ML, Data Science, Security, Design, and client teams. It also embeds healthcare compliance, security, and responsible AI throughout the product lifecycle.

Own the enterprise AI strategy, roadmap, governance, investment priorities, and measurement framework. Identify and prototype high-value AI use cases, guide pilots into production, establish responsible-AI standards for sensitive data, and communicate outcomes to executives. Build adoption through enablement and reusable practices while coordinating technology, business, legal, security, data, and vendor partners. Lead the emerging AI strategy function and hold teams accountable for delivery and value realization.

Advise Product, Engineering, Security, and business teams on software, SaaS, cloud, privacy, cybersecurity, AI, intellectual property, and emerging technology regulations. Guide products throughout their lifecycle, assess legal and regulatory risks, draft agreements and privacy documentation, support privacy-by-design, monitor global regulations, and develop scalable legal processes, playbooks, policies, and training.

Develop and deploy statistical and machine learning models for credit risk, delinquency, defaults, loan losses, underwriting, and risk-based pricing. Analyze credit bureau, alternative, and loan performance data; build production-ready analytical pipelines; monitor model performance; and address data anomalies. Lead complex initiatives, mentor colleagues, document methodologies, and incorporate model governance, fair lending, privacy, and responsible AI considerations while partnering with Credit Risk, Product, Engineering, and Finance.

Drive AI-enabled product strategy and execution by identifying customer opportunities, defining requirements, and partnering with engineering, design, data, sales, marketing, and customers. Lead development of products using LLMs, generative AI, RAG, and intelligent agents; validate roadmap priorities, communicate product vision, support launches, and improve feature adoption. Apply AI tools to accelerate product specifications, workflows, and software delivery.