Top Machine Learning Jobs in Mexico

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.

Develop and publish supply plans aligning customer demand, manufacturing capacity, material availability, inventory, product mix, revenue goals, and customer commitments. Use AI-enabled analytics, ERP systems, and supply planning tools to evaluate scenarios, identify risks, optimize plans, and support data-driven decisions. Collaborate with global cross-functional teams to resolve constraints and communicate supply risks, opportunities, and recommended actions.

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.

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.

Analyzes workforce and healthcare data to identify trends, build predictive and machine learning models, and deliver actionable recommendations. Uses Python, Alteryx, and Power BI for analytics, visualization, and reporting. The role supports workforce strategy, employee engagement, organizational effectiveness, talent management, and healthcare cost insights. Responsibilities also include client collaboration, relationship management, team guidance, and applying risk adjustment and medical economics concepts to inform strategic decisions.

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.

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.

Leads internal audit and SOX engagements, manages client relationships, develops audit methodologies, evaluates financial statements and internal controls, conducts risk assessments, and oversees audit reporting. The role includes project ownership, budgeting, stakeholder management, team leadership, mentoring, process improvement, and use of AI and risk technology to enhance audit effectiveness and address compliance and governance risks.

Supports data and analytics consulting projects through research, data analysis, visualization, predictive analytics, and machine learning tasks. Uses Python, SQL Server Data Tools, Power BI, Tableau, and Alteryx to manipulate data, validate findings, develop visualizations, and generate actionable insights. Collaborates with experienced professionals while learning consulting methodologies, artificial intelligence applications, and data-driven solution development.

Support CAD tools and flows for HBM design, collaborate with design teams, and engage in mixed-signal IC design processes.

Leads the build-out and operation of XDOF’s LATAM robotics data-collection network in Mexico or Brazil. Responsibilities include expanding collection venues, sourcing partnerships, recruiting and managing field teams, deploying and maintaining hardware, optimizing operational efficiency, monitoring data quality, and converting market learnings into reusable SOPs and playbooks.

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.

Build AI systems for legal teams by designing legal datasets, annotating training and benchmarking data, defining model requirements, translating legal workflows into structured processes, validating product capabilities, engaging clients, and prototyping with AI tools and scripting. Requires a JD and active or prior bar membership, 2–8+ years of legal practice, strong familiarity with litigation, investigations, compliance, or e-discovery workflows, and comfort collaborating with engineers and product teams.

Build and support self-service business intelligence and analytics solutions. Develop data models, information marts, semantic layers, and end-to-end pipelines using Databricks and Azure services. Collaborate with data engineers, analysts, data scientists, and business stakeholders to gather requirements, deliver actionable insights, optimize data processing, and ensure data warehouse quality, controls, testing, and scalability. Provide lakehouse support, establish analytics standards, conduct research, and communicate technical concepts to nontechnical users.

Designs, builds, and operationalizes secure, scalable generative AI and machine learning systems across AWS and GCP. Responsibilities include developing LLM, agentic, and RAG applications; architecting serverless, event-driven microservices; managing model experimentation, evaluation, deployment, and monitoring; and optimizing performance and cost. The role also leads responsible AI, HIPAA compliance, governance, and technical collaboration while mentoring engineers and managing team performance.

Lead quality engineering for AI-powered products by designing scalable test automation across API, UI, integration, end-to-end, performance, and AI validation. Define quality strategies, metrics, coverage, and CI/CD quality gates; evaluate AI-assisted testing technologies; drive shift-left practices; and mentor developers and engineers on maintainable automation.

Lead the architecture, development, and production delivery of AI software products and platform capabilities. Collaborate across software development, data science, product management, and domain teams to apply generative AI, agentic systems, and machine learning. Drive scalability, reliability, security, testing, observability, and operational excellence while influencing technical direction across teams. Mentor developers, evaluate emerging technologies, and align engineering solutions with customer and business needs.

Develop, train, evaluate, and optimize ML solutions and data pipelines; translate business requirements into production-ready ML code; perform feature engineering, model selection, and evaluation; document work, participate in code reviews, and support testing and data issue resolution.

Build and own production AI features serving millions of students. Design debuggable agent, tool, and orchestration systems; establish evaluation practices; choose between LLMs, classic machine learning, and deterministic solutions; make defensible architecture decisions; and improve engineering standards through reviews and technical leadership.

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.

Lead post-sale technical and commercial engagement for strategic Salesforce Data Cloud and Agentforce customers. Develop consumption, adoption, enablement, and mutual success plans; guide architecture, governance, integrations, and change management; identify risks; and accelerate platform usage, customer value, and account growth. Build executive relationships, facilitate discovery and workshops, coordinate internal sales, engineering, product, services, and support teams, and communicate product updates. Serve as a trusted technical advisor across CRM, data architecture, analytics, AI, and machine learning.

Lead the Content Foundations engineering team and evolve Scribd’s high-scale document ingestion platform into a real-time, AI-native system. Responsibilities include setting technical strategy, integrating AI and ML capabilities, overseeing distributed data infrastructure, ensuring security and operational reliability, managing the roadmap, hiring and developing backend and AI data engineers, and partnering with product and engineering leaders across the organization.

Own sales analytics and forecasting as the trusted source of truth for sales leadership. Build predictive and machine learning models to assess deal close likelihood and slippage, maintain Salesforce and Tableau dashboards, improve data governance, and translate complex business questions into actionable GTM recommendations. Partner with Sales, Marketing, and Finance on reporting standards, strategic planning, QBRs, and executive insights.

Leads Stripe’s Credit Risk machine learning engineering team, setting technical and product strategy for systems that detect and mitigate credit risk. Owns outcomes including credit losses, profitability, detection quality, and user experience. Oversees delivery of reliable models and decision systems, partners cross-functionally on priorities, and recruits, develops, and manages machine learning engineers while contributing to broader engineering leadership.

Own employee experience products from discovery through delivery, adoption, measurement, and iteration. Define product strategy and roadmaps, connect data and workflows, develop governed AI-enabled and agentic experiences, and partner with People, engineering, data, security, and legal teams. Ensure privacy, permissions, evaluation, human accountability, and user trust while driving adoption and organizational change.