Top Machine Learning Jobs in Spain

Lead software product strategy, roadmap development, requirements definition, product launches, stakeholder engagement, OKRs, performance measurement, and cross-functional collaboration with engineering and business teams. The role requires translating business needs into technical product solutions, prioritizing features, documenting requirements, monitoring adoption and feedback, and driving data-informed decisions across multiple business-critical projects.

Lead high-complexity data science initiatives improving search ranking, retrieval, relevance, and evaluation. Develop and deploy production machine-learning and NLP solutions, investigate LLM-based ranking features, optimize SQL queries, and work with sparse privacy-preserving data. Define strategies for ambiguous problems, guide other data scientists, collaborate with product teams, and own projects from scoping through production.

Seeking Quantitative Finance Experts to train and evaluate AI models by assessing accuracy, reviewing responses, and providing domain-specific feedback. Requires expertise in Quantitative Finance and strong analytical skills.

Research and investigate global fraud trends using BioCatch data and external sources. Produce data-driven insights, visualizations, written thought leadership, dashboards, and presentations. Build repositories and automate analysis workflows while applying fraud expertise and AI tools to validate findings and support sales and marketing.

Lead statistical strategy and delivery for Phase I-IV clinical trials embedded with a pharmaceutical sponsor. Design analyses, support protocol and reporting activities, oversee programming/CRO work, apply SAS/R (R Shiny)/Python and ML/DL methods for analyses and automation, and mentor junior statisticians.

Develops and scales predictive analytics and machine learning models for commercial decision-making. Responsibilities include forecasting, pricing optimization, profitability and tender analytics, customer behavior modeling, opportunity identification, model lifecycle management, and embedding analytical solutions into business processes. Partners with Commercial, Marketing, Finance, and IT stakeholders to translate business needs into scalable, interpretable insights and measurable growth outcomes.

Build and deploy production ML systems for large-scale ecommerce search and recommendations using text, image, and multimodal approaches. Tune and integrate LLMs and transformer models, develop end-to-end visual solutions, improve business KPIs through experimentation, and collaborate across teams to productize and scale ML capabilities.

Build and scale MLOps infrastructure that moves machine learning models from development to production. Responsibilities include model serving, automated training and deployment, CI/CD, monitoring, drift detection, rollback mechanisms, streaming feature pipelines, and agentic workflow integration. The role requires collaboration with data science, platform, and product teams to deliver reliable, observable, low-latency ML systems.

Build and own pricing data science capabilities, including demand and elasticity models, forecasting, willingness-to-pay models, pricing optimization, experimentation, and causal analysis. Deploy and monitor machine learning systems, establish governed pricing metrics, and partner with Commercial, Finance, and Product to improve margin and revenue. Set analytical standards for pricing decisions and build reusable analytical capabilities.

Lead and manage strategic partnerships with medtech, pharma, and technology partners; drive end-to-end partnership processes, translate technical capabilities into partner value, support international expansion, and align internal teams to partnership strategy.

Lead and grow the SAP Digital Core practice in Spain by defining GTM strategy, driving business development, managing end-to-end sales and proposals, and overseeing delivery as Project Director or Program Manager. Build partnerships with SAP and stakeholders, align technical and functional solutions (including AI/IoT) with business needs, and expand market presence through speaking, publications, and targeted campaigns.

Build and enhance RAG systems, AI components, backend services, data pipelines, and REST APIs under senior guidance. Write production code, tests, evaluation harnesses, documentation, and runbooks. Support AWS deployments using containers and CI/CD, investigate model failure modes, and contribute to architectural decisions. The role offers increasing ownership while collaborating with senior engineers, FDEs, FDXs, and enterprise clients in distributed teams.

Build core components of a diagnostic and personalization platform: backend data pipelines, frontends, services for genomic data ingestion, ML model integration, APIs, and cloud infrastructure. Collaborate with product and ML teams, iterate on prototypes and MVPs, and make architectural decisions supporting scalability and reliability.

Lead and develop a team of Data Scientists and ML Engineers building production machine-learning systems for logistics. Drive AI adoption, roadmap prioritization, agile execution, and measurable business outcomes. Partner with Product Managers, Staff ML Engineers, and the MLOps platform team to deploy scalable solutions. The role requires deep hands-on experience with production ML, recommender systems, pricing algorithms, statistical models, and modern data and AI technologies, alongside strong people leadership and executive communication skills.

Design, build, validate, deploy, and monitor predictive lifetime value (pLTV) models for subscription products. Partner with data engineering and cross-functional teams to integrate models into production, run experiments (A/B tests), troubleshoot anomalies, and translate insights into actionable recommendations while adopting best-practice ML techniques.

The role involves developing and optimizing electrochemical measurement methods, mathematical modeling, algorithm development, and leading system validation for diagnostic technologies.

Lead Workday Compensation implementations and HCM transformations: run workshops, gather requirements, configure, test, deploy, lead workstreams, liaise with clients and teams, and apply Accenture/Workday methodologies and AI-enabled tools.

Lead ING's global Security Detection & Response capability: define SDR strategy and roadmap, ensure effectiveness of monitoring, detection, and SOC operations; lead product and service teams; manage partners and budget; drive adoption, regulatory compliance, incident response, AI/ML-driven detection, and measurable cyber risk reduction across the enterprise.

Define and execute the product roadmap for AI-driven blockchain security monitoring tools. Collaborate with cross-functional teams, analyze market and customer feedback, deliver features on time, and communicate with stakeholders to align expectations and improve product offerings.

Perform FP&A and controlling activities including budgeting, forecasting, management reporting, variance analysis, and business partnering. Leverage BI tools and AI-enabled analytics for forecasting, anomaly detection, and automated reporting to support decision-making and financial controls for regional operations.

Develop and productionize computer-vision and ML algorithms for identity document analysis, authentication, and fraud detection. Prototype and build solutions for cloud, server, and mobile deployment, collaborate with engineering and product teams, evaluate and test document-processing models, and support long-term improvements and data curation for robust deployments.

Develop and optimize search and LLM systems for financial applications. Responsibilities include fine-tuning open-source models, preference alignment, inference acceleration, quantization, retrieval optimization, modular AWS SageMaker and Docker pipeline delivery, and automated benchmarking of model quality, latency, and cost.

Design, train, evaluate, and deploy advanced NLP and machine learning models within large-scale production systems. Build integration layers, continuous learning pipelines, evaluation frameworks, monitoring, and automated testing. Optimize models for low-latency inference, reliability, and scale while integrating probabilistic models with deterministic or legacy architectures.

Lead prompt engineering initiatives, coach and support business users on AI tools, create best practices and training, evaluate AI platforms, mentor junior staff, and document technical learnings to integrate AI into workflows.

Lead procurement digitalization by developing strategy and roadmap, assessing technologies (AI/ML/RPA), advising stakeholders, driving AI initiatives, improving processes, and ensuring adoption and ROI across global procurement.