Top Machine Learning Jobs in Spain

Supports Celonis Platform implementations through process-mining discovery, SQL/Python data transformation, AI and machine-learning solution development, and LLM integrations. Participates in customer workshops, translates technical findings into business value, documents solutions, and collaborates with consulting, product, engineering, architecture, and support teams. The role is part of a 12-month graduate program for recent university graduates.

Manage an international credit machine learning engineering team building models for lending decisions across Europe and Australia. Set technical strategy, lead advanced ML solution development, partner with product, engineering, analytics, design, and risk teams, and ensure scalable architecture and operational processes. Mentor engineers, develop talent, provide hands-on technical leadership, and support underwriting systems. The role includes an on-call rotation and requires residence in Spain.

Lead Perk’s 40+ person data organization across data engineering, analytics, data science, governance, and reporting. Own the company-wide data strategy, roadmap, architecture, platform, budget, privacy, compliance, and AI product integration. Partner with executive leaders across Product, Engineering, Finance, Marketing, Revenue, Customer Care, and People to turn data into trusted products, insights, recommendations, and business decisions. Develop senior leaders, scale the organization, and establish reliable, governed data infrastructure.

Drive product and business impact through statistical modeling, experiment design, Bayesian marketing analysis, machine learning, NLP, LLM applications, and classifier implementation. Build growth metrics, monitoring pipelines, evaluation datasets, and AI trigger systems across search and Duck.ai. Analyze privacy-constrained data, deploy production-grade models, optimize SQL queries, and lead complex projects from scoping through production with minimal direction.

Leads QIMA’s virtual pharmacology models program by evaluating biological datasets, selecting high-potential projects, building data pipelines and machine learning proof-of-concepts, and preparing models for scientific validation and commercialization. The role requires collaboration with laboratory scientists, business leaders, IT and AI teams, with responsibility for reproducible code, documentation, data quality, experimental design and clear communication to technical and nontechnical audiences.

Leads JetBrains Air’s data science, analytics, and data engineering functions. Builds and develops multidisciplinary teams, establishes scalable data foundations and metric frameworks, drives experimentation, and delivers insights for product, marketing, acquisition, retention, and monetization. Partners with engineering and leadership on data strategy, infrastructure, machine learning capabilities, privacy, security, and responsible data use.

Leads developer relations and technical partner engagement across EMEA AI-native ecosystems. Advises developers, software providers, and startups on integrating NVIDIA platforms, creates technical enablement resources, supports onboarding, and identifies adoption opportunities. Collaborates with engineering, product, marketing, and solution architecture teams to solve technical challenges, deliver workshops, influence product roadmaps, and scale developer programs and communities.

Develop and operationalize AI, machine learning, and advanced analytics solutions for third-party cyber risk management. Responsibilities include dynamic vendor risk scoring, predictive analytics, anomaly detection, LLM and agentic AI applications, model lifecycle ownership, deployment, monitoring, validation, explainability, and governance. The role partners with cybersecurity, third-party risk, and data engineering teams to deliver scalable, production-ready solutions and communicate risk insights to technical and non-technical stakeholders.

Supports Nissan’s NVH team by analyzing vehicle test data and developing machine learning models to predict tyre noise. Responsibilities include investigating tyre and vehicle parameter effects, building an interface for noise simulation, enhancing a RAG application with technical parameters, and helping digitalize NVH engineering processes. The role is a hybrid internship suitable for students completing a bachelor’s final project or master’s thesis.

Supports data science and data engineering projects for clients by applying machine learning, analytics, programming, data pipelines, warehousing, modeling, governance, and data quality practices. Collaborates with data scientists, analysts, engineers, and strategists to create data-driven business solutions, participate in code reviews, and solve complex client challenges.

Advise city governments across the Americas on designing, prototyping, and launching digital services. Guide system and data architecture, DevOps, infrastructure, AI and machine learning decisions, rapid prototyping, and end-to-end product delivery. Work hands-on with multidisciplinary teams while coaching clients and influencing technical decisions without direct authority. The role requires senior technical leadership, strong communication, and professional English plus Spanish and/or Portuguese.

Analyze player behavior and business performance to support user acquisition, retention, and monetization decisions. Build data models, pipelines, Power BI dashboards, predictive analytics, segmentation models, and A/B test analyses. Apply machine learning and Generative AI to analytics use cases, maintain analytical workflows with GitLab and CI/CD practices, and communicate insights to senior stakeholders. The role also promotes analytical best practices across the organization.

Lead development of credit risk stress-testing models across retail and non-retail portfolios. Apply statistical, econometric, predictive, and macroeconomic modeling to assess portfolio vulnerabilities, credit losses, and risk resilience under changing economic conditions. Use R, Python, SQL, Teradata, and AWS to analyze complex data, explain model assumptions and limitations, support validation and planning, guide analysts, and provide actionable insights to risk and business stakeholders.

Provides technical leadership for secure, resilient cloud-native content platforms. Responsibilities include designing and operating AWS and Kubernetes solutions, developing software and automation, integrating AI capabilities, improving CI/CD and infrastructure-as-code practices, embedding testing and observability, strengthening reliability and disaster recovery, and mentoring engineers. The role supports the migration and ongoing enhancement of OpenText document retention and archiving services.

Leads end-to-end data science solutions for clinical development and trial operations. Responsibilities include predictive modeling, optimization, simulations, generative AI, real-world data analytics, LLM-enabled workflows, model validation, MLOps, responsible AI, stakeholder communication, solution adoption, and mentoring. The role supports study feasibility, enrollment forecasting, site selection, protocol design, recruitment, retention, comparative evidence, and operational planning.

Develops scalable machine learning solutions for personalized recommendations, learning to rank, and relevance optimization. Responsibilities include data preprocessing, model deployment, A/B testing, experimentation, algorithm improvement, and cross-functional collaboration. The role requires communicating technical results to diverse stakeholders and working across the technology stack to deliver high-throughput, low-latency ML systems for Expedia Group’s B2B travel business.

Develop and deploy AI and machine learning methods for clinical trial design, dose optimization, biomarker discovery, digital twins, predictive modeling, and safety and efficacy detection. Partner with clinical, biometrics, and regulatory teams to validate and adopt models, build reusable data products, and establish evaluation frameworks for clinical and regulatory use. Contribute to scientific publications, presentations, and AI innovation across pharmaceutical development programs.

Owns fraud product experiences across the Veriff Network, including a Feedback API, customer fraud insights, automated reporting, and shared-signal capabilities. The role leads fraud product strategy, roadmap prioritization, customer discovery, network feedback loops, data and ML product development, commercial packaging, and collaboration with fraud operations, sales, data science, and product leadership.

Develops advanced generative AI, machine learning, optimization, and agentic AI solutions for life sciences. Translates technical outcomes into business value, engages senior stakeholders, supports consulting-style client initiatives, and collaborates across AI, product, strategy, and UX teams to deliver measurable pharmaceutical and clinical-trial impact.

Develop advanced GenAI, machine learning, optimization, and agentic AI solutions for life sciences and pharmaceutical use cases. Translate technical outcomes into business value, present initiatives to senior stakeholders, manage client engagements, and collaborate across AI, product, strategy, and UX teams to deliver measurable digital transformation.

Lead applied AI and data science initiatives from experimentation through production. Develop machine learning, NLP, generative AI, LLM, retrieval, RAG, and agent-based solutions using Python, SQL, APIs, and data pipelines. Design evaluation frameworks, work with large-scale structured and unstructured data, and collaborate on deployment, monitoring, and infrastructure. Contribute to responsible AI practices, technical standards, reusable components, and mentoring while translating business problems into scalable technical solutions.

Leads a data engineering chapter supporting financial crime platforms. The role combines hands-on solution design, architecture, coding, technical reviews, troubleshooting, and delivery of trusted data products with people leadership, coaching, standards, and capability development. Responsibilities include modernizing data platforms using AWS and Snowflake, strengthening data quality and controls, advancing DevSecOps and observability, and partnering across risk, compliance, technology, governance, and business teams.

Contributes to developing, testing, debugging, documenting, and validating computer vision software for parcel management and automated logistics systems. Works with image data, camera inputs, software services, prototypes, and AI-based perception capabilities alongside experienced engineers. Collaborates across software, architecture, product, and automation teams while improving software reliability and performance. The role provides structured development and hands-on exposure to AI, computer vision, robotics, and industrial automation.

Develop and deploy machine learning models and scalable data pipelines for pharmaceutical sciences and translational safety. Build AI-ready datasets from toxicology, PK/PD, in vivo, biomarker, pathology, and assay data. Collaborate with toxicologists, pharmacologists, safety professionals, and data engineers to support safety signal detection, dose selection, translational research, and scientific decision-making. Apply model governance, data standardization, ontology practices, validation, and cloud-based engineering techniques.

Leads the design of trusted, reusable, AI-ready data assets for small-molecule discovery. Transforms molecular, reaction, synthesis, purification, and assay data into scalable analytical workflows and model-ready datasets. Partners with medicinal chemists, automation scientists, data engineers, and technology teams to establish data standards, quality frameworks, metadata, lineage, and reproducible research practices. Applies data science and machine learning to support scientific decision-making, digital laboratories, and AI-enabled drug discovery while mentoring colleagues and providing technical leadership.