Top Machine Learning Jobs in Barcelona

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.

The AI Engineer will build data pipelines and improve ML systems for reliability in production. Responsibilities include schema design, data infrastructure, and model evaluation, ensuring high-quality outputs and integration with product workflows.

Analyze fraud patterns and financial risk using SQL, Python, Looker, and BigQuery. Design, test, monitor, and optimize fraud prevention controls; evaluate A/B tests; support machine learning workflows; maintain fraud escalations; and build executive dashboards tracking fraud, exposure, and compliance metrics. Collaborate with Product, Engineering, Data Science, local operations, leadership, and external partners to improve profitability and reduce fraud losses.

Develops GenAI, machine learning, NLP, and LLM solutions for clinical trial operations. Responsibilities include protocol analytics, trial similarity assessment, data harmonization, eligibility evaluation, enrollment forecasting, cost optimization, country and site selection, patient modeling, and enrollment simulations. The role requires communicating technical findings to diverse stakeholders and collaborating across healthcare and clinical operations teams.

Leads end-to-end AI research and technical delivery for clinical-development applications across oncology and biopharmaceuticals. Develops and evaluates advanced models, multimodal systems, and agentic workflows for trial design, analysis, patient stratification, biomarkers, and treatment-effect research. Requires strong Python and modern machine-learning expertise, reproducible research practices, scientific communication, and technical leadership. The role includes mentoring, cross-functional influence, publications, external representation, and responsible AI governance in a regulated pharmaceutical environment.

Analyze large-scale player data using Python and SQL, engineer features, and develop machine learning models and prediction pipelines to improve game revenue. Conduct experimental design, causal inference, and A/B testing, then present actionable findings to non-technical game teams. The role involves collaborating with global data and game development teams and delivering projects from exploratory analysis through deployed models.

Analyze player behavior and business performance across user acquisition, retention, and monetization. Build reliable data models, pipelines, Power BI dashboards, and reports using modern data tools. Support A/B testing, predictive analytics, behavioral segmentation, machine learning, and Generative AI initiatives. Communicate actionable insights to stakeholders, maintain analytical code in GitLab, troubleshoot data issues, and promote best practices across the organization.

Lead product vision and discovery for a genomics-driven diagnostic and personalization platform. Validate hypotheses with patients, clinicians, and partners; translate research into product specs; coordinate labs and data providers; partner with engineers and researchers to build AI prototypes; shape data, regulatory, and GTM strategy while making core product and architectural decisions.

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.

A full-time 6–12 month internship in Barcelona supporting AI and data projects across machine learning, data science, AI engineering, data engineering, bioinformatics, clinical data, and analytics teams. Interns will assist with analysis, reports, workshops, presentations, and collaborative innovation initiatives within AstraZeneca’s biopharmaceutical environment.

Own and operate infrastructure security engineering across detection engineering, incident response, AWS and Kubernetes security, endpoint hardening, DLP, vulnerability management, and security automation. Build SIEM detections, ML-based analytics, and LLM-powered workflows while investigating incidents and driving remediation. Secure Linux, macOS, and Windows environments, perform network analysis and penetration testing, and partner with engineering teams to improve security across production infrastructure.

Own the vision, roadmap, commercialization, and executive strategy for Kaltura’s AI-powered learning and enablement platform. Lead 0-to-1 MVP delivery and post-MVP expansion across AI course generation, conversational tutoring, personalized learning, simulations, and enterprise foundations. Integrate acquired products and agentic AI capabilities while partnering with engineering and GTM teams on architecture, pricing, packaging, positioning, and enterprise sales. The role requires deep B2B SaaS product leadership, enterprise learning platform experience, production AI/ML delivery, and strong technical and commercial judgment.

Develop data science solutions for client onboarding, including data processing, modeling, clustering, prediction algorithms, and statistical evaluation. Support R&D of automated data-driven processes and features, perform rapid analyses and scripting, and translate commercial and pricing questions into machine learning solutions. The role requires Python, modern statistics and machine learning knowledge, strong English communication, and comfort working independently in a fast-paced startup environment.

Owns the product vision, roadmap, backlog, and software development lifecycle. Partners with engineering, customers, and stakeholders to prioritize features, incorporate feedback, deliver documentation, launch products, monitor performance, and drive adoption. The role requires product ownership experience in software and highly regulated industries such as pharmaceuticals, biotech, or healthcare, along with knowledge of Agile methodologies and applicable GMP and ISO regulations.

Conduct advanced research in artificial intelligence and machine learning, including deep learning, graphical models, reinforcement learning, computer perception, natural language processing, and data representation. Define complex business problems, design and deliver modeling solutions, evaluate model performance, establish scientific and engineering specifications, and build analytical roadmaps. Assess production implementation requirements, improve data capture methods, apply statistical and mathematical techniques, and resolve root causes and process bottlenecks.

Own the strategy and execution of integrated B2B marketing campaigns for enterprise AI solutions and a self-service platform. Drive pipeline, product adoption, acquisition, and category awareness through ABM, content, events, paid media, lifecycle, social, partnerships, PR, and sales activation. Coordinate internal teams and agencies, optimize messaging and conversion paths, and measure performance across account engagement, pipeline, product adoption, and awareness.

Supports data integration, systems architecture, and solution development on the Aizon platform for pharmaceutical manufacturing clients. Responsibilities include gathering requirements, designing data management strategies, integrating pharma systems, supporting solution architects, participating in projects and proofs of concept, proposing platform improvements, and collaborating with technical teams and customers. The role also requires learning biotechnology processes and pharmaceutical regulations, translating business needs into technical requirements, and traveling to client sites.

Design and deliver internal systems, Go services, APIs, data models, dashboards, visualizations, and LLM-powered workflow features. The role includes writing specifications, estimating work, code reviews, testing, documentation, security, performance, accessibility, and collaboration with nontechnical stakeholders. The engineer will handle sensitive people data, mentor colleagues, and improve team practices in a globally distributed environment.

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.

Own the anti-fraud product roadmap across signup, payments, account protection, compute-abuse prevention, risk scoring, manual review, customer verification, and appeals. Partner with Trust & Safety, Engineering, Data Science, Analytics, and Legal to define detection rules, models, incident responses, experiments, and compliance processes. Monitor fraud losses, chargebacks, false positives, review throughput, and detection speed while minimizing customer friction.

Develop machine learning models, detection rules, and data-driven solutions for web security products. Analyze massive customer datasets, improve detection accuracy, enhance production data processes, troubleshoot distributed systems, and collaborate with engineering and data science teams to address customer challenges and guide product improvements.

Lead and develop Customer Success Managers across Southern Europe, especially France and Spain. Ensure enterprise customers successfully launch and derive measurable value from the product, build executive and technical stakeholder relationships, translate product capabilities and performance data into business outcomes, establish scalable customer success processes, and handle complex escalations.

Lead the vision, roadmap, architecture, and execution of a centralized machine learning platform for high-volume banking applications. Own MLOps infrastructure, model evaluation, governance, monitoring, retraining, and real-time inference capabilities. Partner with engineering, data science, security, legal, and regulatory stakeholders to ensure resilient, explainable, compliant systems. Lead multi-squad initiatives, establish product practices, and mentor other Product Managers.

Develop quantitative models for sports pricing, odds compilation, and risk management. Analyze large datasets, identify trading insights, deploy models with engineers, monitor performance, conduct back-testing, and continuously improve model accuracy. Collaborate with data scientists and engineers to align models with business needs. The role requires Python, sports betting market knowledge, statistical modeling, and experience with odds and margin optimization.