Top Machine Learning Jobs in Mexico

Provides analytics and strategic advisory support for mergers, acquisitions, and other business transactions. Analyzes contract data to identify risks, obligations, performance insights, and compliance indicators; develops algorithms, data models, pipelines, dashboards, and predictive models using Python and machine learning. Collaborates with clients and internal teams, validates data integrity and security, mentors junior staff, and delivers data-driven recommendations.

Operate and evolve SAN/SAR tooling (e.g., FSM/M5), troubleshoot incidents, monitor system health, translate business needs into configurations, prepare test/production environments, support migration to Dynamics 365, and identify automation/AI opportunities while improving procedures and documenting solutions.

Leads analytics-focused consulting engagements for global clients, developing data strategies, analytical solutions, and business recommendations. Manages senior stakeholder relationships, leads project teams, presents insights, collaborates with Mastercard partners, and mentors junior consultants. The role applies quantitative and qualitative techniques using large datasets, AI, machine learning, BI platforms, and analytics technologies to deliver measurable client impact.

Operate AI-assisted identity migration and demonstration pipelines, review and validate LLM-generated analyses and configurations, and remediate SailPoint Identity Security Cloud environments. Troubleshoot pipeline and product issues, create sales-ready deliverables, support proof-of-value engagements, and improve agentic tools, prompts, playbooks, and evaluation processes. Collaborate with sales, solution engineering, product, professional services, customer success, and forward-deployed engineering teams.

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.

Lead teams applying advanced data science and machine learning to large-scale claims, clinical, and member data to generate actionable insights that reduce cost of care and improve population health. Manage client relationships, mentor staff, ensure project delivery and quality, develop payer-focused analytics products, present findings to senior stakeholders, and promote continuous improvement and knowledge sharing.

Leads data analysis and advanced analytics workstreams for clients, using statistical modeling, machine learning, data visualization, and Adobe Experience Cloud technologies to improve operations and strategic decision-making. Manages project planning, budgeting, execution, and completion while mentoring teams, maintaining data integrity and security, building Tableau and Power BI dashboards, identifying business opportunities, and integrating innovative analytics practices.

Leverage advanced analytics, data manipulation, visualization, statistical modeling, predictive analytics, and machine learning to generate client insights and recommendations. Apply Adobe Experience Cloud solutions to support business transformation and strategic decision-making. Build client relationships, manage and inspire teams, navigate complex engagements, uphold professional standards, and deliver quality outcomes while developing technical and consulting expertise.

Design and implement Industry 4.0 solutions (IoT, AI/ML, robotics, AR/VR, 3D printing, Big Data) to optimize operational and office processes. Integrate with ERP (SAP), MES, PLCs, robots and databases, ensure SOX compliance, support cloud and on-prem deployments, and develop BI/analytics solutions.

Develop, design, and troubleshoot software components as part of an agile team. Write secure, high-quality code, apply SDLC and automation tools, analyze data to identify issues, and contribute to system stability and scalability while learning cloud, AI/ML, and mobile technologies.

Lead and manage internal audit and SOX engagements, implement audit methodologies, use AI and risk technology to optimize processes, assess controls and financial statements, mentor staff, produce audit reports, and collaborate with stakeholders to mitigate risks and support client growth.

Build and deploy statistical and ML models (especially demand forecasting) to improve sales forecast accuracy. Analyze data, create actionable insights, collaborate with stakeholders and regional teams, set analytics standards, monitor model performance, and deliver dashboards and visualizations for supply chain decision-making.

Build and operate AI platforms, tooling, infrastructure, and machine learning development environments across AstraZeneca. Collaborate with data scientists and machine learning engineers, automate lifecycle tasks, improve scalable workflows, adapt machine learning methods for distributed and GPU environments, and transition research into production. Partner with cybersecurity and data privacy teams to maintain secure, compliant systems while enabling productivity.

Develop experimental AI techniques for agentic marketing applications, especially image and video generation. Build proofs of concept, autonomous evaluation and improvement systems, realistic AI-generated video, and brand-aligned creative generation workflows. The role requires strong quantitative and probabilistic thinking, backend architecture skills, creativity with LLM applications, and product intuition.

Lead a team of full-stack product and data engineers building personalized, high-converting checkout experiences. Drive experimentation, ML-powered optimization, data platform development, technical architecture, roadmap planning, incident response, cross-functional delivery, talent recruitment, and engineering team mentorship.

Leads product growth and digital insights for a business application, owning local product strategy, roadmap prioritization, discovery, requirements, and stakeholder alignment. Manages an AI and machine learning personalization squad developing recommendation, cross-sell, and upsell models. Owns data management, analytics, experimentation, impact measurement, and business insights to support data-driven decisions. The role bridges business, technology, and data teams across regional environments.

Leads the plant’s automation, controls, robotics, and digitalization portfolio for blade manufacturing. Responsibilities include designing and specifying automated systems, programming robotic and PLC-based controls, modeling solutions, managing suppliers, executing validation and commissioning, completing safety assessments, and handing systems over to Production and Maintenance. The role establishes technical standards, applies manufacturing readiness frameworks, uses analytics, SPC, and AI-based process monitoring, manages capital projects, mentors technical teams, and supports continuous improvement and operational excellence.

Leads the Advanced Manufacturing Engineering team and molding technology strategy for composite blade manufacturing. Directs process development, testing, validation, industrialization, automation, data analysis, technology roadmaps, supplier performance, and capital planning. Oversees molding processes including mold preparation, gel coat, fiber layup, resin infusion, curing, and bonding. Develops engineers, manages project portfolios, ensures technical documentation and change control, and drives safety, quality, cost, productivity, and operational excellence improvements.

Develop product positioning, messaging, content, and go-to-market assets for an AI training data platform. Lead end-to-end product, vertical, DataLab, and commercial launches; translate complex AI and data concepts for technical buyers; create thought leadership, case studies, decks, webinars, videos, social content, and landing pages; and partner with Product, Sales, DataLab, Community, and business leaders to strengthen market narrative and support commercial growth.

Lead the strategic roadmap and business integration of CVS Health’s Clinical Insights Engine across screening, diagnosis, quality measurement, or care management use cases. Partner with platform product managers, clinical leaders, business owners, and technology teams to align clinical needs with scalable platform capabilities. Drive adoption, value realization, KPI monitoring, business cases, and continuous product improvement while ensuring measurable clinical and financial outcomes.

Develop and improve machine learning products focused on fraud detection and prevention across lending and payments. Own models end to end, including experimentation, feature engineering, production pipelines, monitoring, and continuous improvement. Partner with Product, Engineering, Risk, and Business teams, apply analytics and emerging AI tools, influence product development, mentor team members, and improve testing and technical quality for production models.

Supports and improves information systems services for ABB’s manufacturing operations. Responsibilities include defining and documenting IT processes, maintaining service information, measuring and reporting service performance, advising users, and supporting IS service installations and changes. The role provides enterprise end-user support involving network services, server infrastructure, Windows 11, Microsoft 365, and factory IT programs, while working independently or collaboratively in an on-site environment.

Senior-level engineer delivering analytics and ML-Ops solutions for healthcare payers. Design and implement analytics platforms on Databricks, develop CI/CD and provisioning patterns, collaborate with data scientists, mentor juniors, and build client relationships to drive actionable insights and operational improvements.

Advise clients on antitrust eDiscovery compliance using machine learning and generative AI. Build and validate TAR/AI models (Epiq AI, Relativity, Reveal, Brainspace), craft LLM prompts, draft defensible protocols, train/transfer workflows to review teams, and support client-facing, pre-sales, and thought-leadership activities. Must manage multiple projects and meet deadline-driven schedules.

Designs, develops, integrates, and deploys intelligent solutions using machine learning, deep learning, generative AI, and LLMs. Builds production-ready AI applications, RAG pipelines, and NLP systems; manages model evaluation, optimization, monitoring, versioning, and deployment. Collaborates with engineering, product, and data teams in a distributed environment while emphasizing scalability, maintainability, testing, observability, responsible AI, and cost efficiency.