Top Machine Learning Jobs in Spain

Lead and mentor a data science team developing machine learning systems for threat detection and agentic AI. Set ML technical strategy, standards, roadmaps, and deployment practices; guide projects from research through production; collaborate with security, engineering, and product teams; develop advanced malware and behavioral threat models; communicate insights; and contribute to research, publications, patents, and hands-on technical work.

Leads analytical methodology and evaluation for AI-based healthcare and pharmaceutical workflows. Owns model selection, cohort and HCP segmentation, agent decision logic, and evaluation frameworks. Partners with engineering, data architecture, and governance teams to productionize rigorous analytical methods, identify new data and techniques, communicate insights, and mentor data science and engineering colleagues.

Leads data science methodology and advanced analytics for AI-enabled Medical Affairs workflows. Develops and evaluates statistical, machine-learning, NLP, and AI models using healthcare and real-world data; defines validation, monitoring, and decision frameworks; partners with engineering, governance, and business teams to operationalize solutions. Provides technical leadership, mentorship, documentation standards, and strategic guidance while ensuring privacy, compliance, responsible AI use, and clear communication of analytical findings.

Leads ad hoc and exploratory analyses for medical and business questions across a global Medical Insights organization. Prepares and validates datasets, translates ambiguous stakeholder needs into decision-ready findings, and maintains analytical documentation. Tests new data sources, methods, and AI-enabled approaches through proof-of-concept analyses to guide platform investments and roadmap decisions. Partners with business and product stakeholders while supporting compliant, reproducible use of regulated data.

Design, deliver, and continuously improve scalable production AI solutions using LLMs, RAG, agentic AI, machine learning, and document intelligence. Build cloud-native systems on Azure using software engineering, architecture, DevOps, and MLOps practices. Own AI platforms and models across deployment, monitoring, security, governance, and optimization. Lead technical initiatives, collaborate with product and business teams, mentor engineers, and translate business needs into measurable outcomes.

Develop and maintain enterprise cybersecurity strategy, roadmaps, target-state architectures, and security standards. Advise executives and technical teams on cloud, data, AI, application, infrastructure, identity, privacy, and regulatory security. Lead secure AI strategy and governance, assess emerging threats and technologies, guide cybersecurity investments, and support transformation, modernization, and acquisition initiatives.

Interns on Oliver Wyman’s Data & Analytics team will develop analytical solutions, machine learning prototypes, and data-driven models for global clients. Responsibilities include designing and validating NLP, GenAI, risk, churn, promotion, and decarbonization analytics solutions, collaborating with clients and colleagues, and explaining technical concepts clearly. The role offers coaching, international exposure, and experience across industries including financial services, energy, retail, and transportation.

Develop enterprise AI and data strategies, value cases, operating models, platform blueprints, Responsible AI roadmaps, and commercialization opportunities. The role involves assessing organizational maturity, translating data into business insights, leading client workstreams, managing large-scale transformations, engaging senior stakeholders, and supporting new deals and go-to-market activities, particularly across supply chain and engineering functions.

Analyze fraud patterns and risk metrics using SQL, Python, Looker, and BigQuery. Design and evaluate fraud prevention controls, support A/B testing, collaborate with Product, Engineering, and Data Science, validate machine learning outputs, manage fraud escalations, and build executive dashboards tracking fraud, financial exposure, and compliance metrics.

Advise clients on creating and capturing value from AI and data. Define enterprise and function-specific strategies, maturity assessments, operating models, platform blueprints, business cases, investment roadmaps, and Responsible AI plans. Lead AI-driven transformations, workstreams, client stakeholder engagement, solution commercialization, and go-to-market activities. Apply modern AI concepts including machine learning, generative AI, LLMs, RAG, and AI governance across supply chain and engineering environments.

Lead data strategy, architecture, governance, quality, compliance, and technology adoption. Translate stakeholder needs into scalable data initiatives, support advanced analytics and AI/ML, promote data literacy, and guide cross-functional teams toward data-driven decision-making.

Drive enterprise sales growth for IQVIA’s AI-powered pharmacovigilance and drug safety technology across EMEA. Responsibilities include prospecting, developing strategic accounts, managing the full sales cycle, exceeding quotas, engaging senior safety stakeholders, and collaborating with product, consulting, hosting, and managed services teams. The role requires experience selling complex SaaS, technology, consulting, and managed services solutions to pharmaceutical, biotech, medical device, and clinical research organizations, with strong knowledge of drug safety and pharmacovigilance.

Develop machine learning, statistical, generative AI, and agentic solutions for home lending customer experiences. Responsibilities include customer cohort optimization, predictive modeling, experimentation, evaluation framework development, model deployment and monitoring, responsible AI, and translating complex analysis into commercial outcomes.

Build and maintain Ergodic AI’s AI platform across back-end and front-end systems. The role involves developing with Python web frameworks and JavaScript/TypeScript, integrating with client technology stacks and workflows, applying AI, ML, and LLM concepts, and contributing to DevOps, cloud deployment, testing, and infrastructure practices. As an early-stage hire, the engineer will help shape the product and solve complex data and AI challenges.

Develop AI-augmented blockchain risk and threat intelligence systems, including autonomous monitoring and investigation workflows. Lead forensic investigations into crypto theft, fraud, and market manipulation. Research post-quantum cryptography and quantum threats to blockchain security, evaluate migration paths, and collaborate with internal teams, external stakeholders, and academic institutions to deploy emerging security tools.

Conduct research at the intersection of formal methods, AI, and scientific reasoning. Develop tools for theorem retrieval, library search, formalization assistance, Lean metaprogramming, and verification pipelines for scientific equations. Collaborate with researchers to define impactful projects, conduct publishable scientific work, and contribute to open-source projects involving AI-assisted physics formalization.

Design and build a unified Kubernetes-based application platform for hybrid, multi-cluster, multi-region, and multi-cloud environments. Develop controllers, operators, node daemons, automation, control planes, scaling systems, and failover capabilities that support millions of pods. Improve reliability, performance, cost, latency, security, governance, and observability through resilient multi-tenant systems, automated remediation, and SLO-driven controls. Partner with product leaders and customers to create reusable platform primitives and developer-friendly experiences.

Lead the technical strategy, architecture, roadmap, and delivery of a cloud-native Engineering Data Platform. Establish governed data products supporting engineering intelligence, operational resilience, security automation, developer experience, and AI-enabled engineering. Drive data quality, lineage, security, compliance, reliability, DevSecOps, DataOps, observability, and automation while influencing senior stakeholders and mentoring engineering teams.

Build and deploy production-grade AI tools using LLMs, agentic systems, RAG pipelines, vector databases, and automated evaluation frameworks. Develop flexible AI services and full-stack integrations with Python, TypeScript, and modern frontend frameworks. Take solutions from prototype through production, measure business impact, collaborate with stakeholders, and extend reusable architectures across use cases.

Operationalize and scale machine learning, generative AI, and agentic solutions for biologics discovery. Responsibilities include building data and deployment pipelines, managing model lifecycles, implementing CI/CD, versioning, monitoring, observability, governance, access controls, and scalable compute. The role partners with data scientists, AI/ML practitioners, technology teams, and scientific experts to move models from experimentation into reliable production environments.

Leads global R&D resource management strategy, operating models, workforce planning, and portfolio analytics across pharmaceutical development. Oversees Planisware algorithm optimization, AI/ML-based FTE forecasting, Named Allocation deployment, dashboards, scenario modeling, and actuals-versus-forecast analysis. Partners with R&D leaders, finance, governance committees, and executives to resolve capacity constraints, prioritize investments, and align resources across the therapeutic portfolio. Directs and develops resource management and analytics teams.

Trainee supporting Data and AI initiatives through data preparation, quality validation, ETL, reporting, AI Assistant maintenance, and machine learning and generative AI testing. The role includes participating in proofs of concept, documenting findings, and collaborating with business, IT, and international stakeholders while learning under experienced mentorship.

Provides IT quality assurance and compliance expertise across Diagnostics IT systems. Responsibilities include CSV, GDPR, data integrity, and ICFR guidance; validation and risk oversight; SOP and quality framework development; audits and control definition; and coaching teams on built-in compliance. The role supports agile delivery using Scrum and SAFe while applying compliance practices to cloud, RPA, machine learning, and DevOps environments.

Analyze and improve production machine learning models powering a high-frequency mobile and connected-TV advertising platform. Responsibilities include investigating model behavior, analyzing auction dynamics, designing experiments and A/B tests, validating hypotheses, identifying performance bottlenecks, and proposing model or feature enhancements. The role requires strong applied data science, deep neural network expertise, statistical rigor, and Python and SQL proficiency.

Leads global early-asset forecasting, commercial market insights, and business-development investment analysis across respiratory and immunology portfolios. Builds patient-based models, scenarios, and risk-adjusted forecasts; supports licensing, partnerships, and acquisitions; advises executives on portfolio and deal decisions; oversees market research; and advances responsible AI, automation, and model governance.