Top Data Science Jobs in Barcelona

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.

Lead end-to-end ML product roadmaps and initiatives (e.g., Attachment Matcher, Transaction Categorizer). Collaborate with data scientists, engineers, and stakeholders to define, deliver, and monitor ML solutions that drive measurable business value for SMEs across Qonto's platform.

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.

Advise C-level life sciences and healthcare executives on AI and data strategies, lead large-scale digital transformations, develop client solutions, and guide implementation and scaling. Build new business opportunities, manage long-term client relationships and multimillion-dollar portfolios, lead cross-functional engagement teams, and ensure high-quality analytical deliverables. Contribute to practice development and organizational growth.

As a Fraud Data Analyst, you will architect real-time defense strategies against payment fraud, develop precision rules, optimize decisioning stacks, and report performance metrics to senior leadership while ensuring a frictionless user experience.

Own data science projects from business questions through customer-facing AI products. Build, evaluate, deploy, and maintain machine learning models; advise internal teams; research advanced AI applications; support important data domains; and collaborate with stakeholders to assess production readiness. The role requires Python-based machine learning development, cloud orchestration, generative AI experience, production deployment experience, and strong communication skills.

Fully funded three-year postdoctoral fellowship focused on developing and improving scientific services for research infrastructures. Fellows conduct technology-development projects aligned with EMBL research areas, receive training in infrastructure operations and management, and build professional networks through secondments and job shadowing. Projects may involve computational science, imaging, optical engineering, robotics, mechanics, biotechnology, or omics. Applicants must hold or have defended a Ph.D. by September 30, 2026, possess relevant STEM training and technology-development experience, and demonstrate strong research potential.

Data Science Intern supporting mobility demand forecasting, fleet optimization, user feedback analysis, and other machine learning use cases. Responsibilities include exploring datasets, developing and evaluating models, feature engineering, deploying proofs of concept, monitoring production models, and communicating findings. The role requires Python, data science libraries, SQL, statistics, machine learning fundamentals, Git, and professional communication in Spanish, French, and English.

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.

Develop statistical and machine learning solutions for customer and energy-transition challenges, from problem discovery and experimentation through prototype validation and production handover. Lead end-to-end analytical workstreams, collaborate with customers, engineers, domain experts, and product teams, and mentor data scientists. Build models for time-series and text data using Python and data science libraries, working with databases, APIs, pipelines, Kubernetes, Linux, Git, and Docker.

Leads clinical data science delivery and team development, designing advanced statistical models and machine learning methods for clinical research. Oversees data analysis, management, regulatory compliance, and trial optimization while collaborating with clinical, biostatistical, and regulatory teams. Communicates complex insights to senior stakeholders and external partners and drives innovation across clinical development programs.

Lead GTM strategy and product positioning for key products, create messaging and content, run campaigns and launches, analyze market and competitive trends, and collaborate cross-functionally to drive adoption, pipeline growth, and customer engagement.

Lead and mentor a data science team building ML and LLM solutions for cybersecurity. Translate business needs into research and production ML objectives, prioritize roadmaps, collaborate cross-functionally, and drive technical vision, model development, and operationalization for malware detection and threat analysis.

Seeking an independent board director with expertise in ML-driven products and data platforms. The role focuses on guiding the company's data-driven culture and product strategies, not on day-to-day operations.

Lead the technical and product vision for the UA DSP, unifying Data Science, Data Engineering, Analytics, BI, and bidding/prediction backend. Own roadmap from bidding/prediction to reporting, manage and grow a ~20-person cross-functional team, partner with GTM and sales, and drive transition from managed-service to self-service advertiser tooling.

As the Director of Data Science, you'll mentor teams, oversee projects, drive data-driven decisions, and implement AI and ML solutions while enhancing our data pipeline.

Lead strategy and roadmap for BidMachines supply-side product. Own product lifecycle, collaborate with engineering, sales, publishers and data teams, analyze supply performance, and drive features for inventory management, auction dynamics, and demand-supply matching to maximize publisher revenue and advertiser satisfaction.

As an AI Director, lead AI and data programs for Specialty Chemicals, drive account growth, mentor teams, and engage C-level stakeholders strategically.

Remote AI/ML Architect to assess current AI/ML landscape, document requirements, and propose SDLC, governance, and deployment approaches. Provide recommendations to architecture and ICT management. Onboarding in the Netherlands (first week). Candidates must be based in Europe with a valid work permit.

The Lead Golang Software Engineer develops server solutions, leads technical designs and code reviews, mentors colleagues, and ensures the quality of engineering practices in the Commercial Systems unit.

The Senior Product Manager will lead the strategic roadmap, conduct market research, manage monetization strategies, and mentor a team while collaborating across various departments.

As a Data Science Associate, you will analyze data for various domains, create data management pipelines, interact with clients, and mentor team members.

Join Vizzuality's talent pool to contribute to sustainability efforts through science, data, design, and project management roles. Apply with your CV and cover letter to be considered when vacancies arise.

Data Scientists at QuantCo will analyze data, build machine learning models, and communicate results to stakeholders, aiming for impactful solutions.

As a Data Science Intern, you'll identify frameworks for data-driven business challenges, transform datasets, run analyses, build machine learning models, and communicate results.