Top Machine Learning Jobs in Lisbon

Leads the product strategy, roadmap, and scaling of Mastercard’s Global Portfolio Optimization capabilities. Converts analytics and business insights into technology-enabled products, recommendations, and workflows through the Mastercard Intelligence Centre. Defines the organization’s AI strategy, evaluates automation and emerging technologies, develops business cases and KPIs, and guides cross-functional teams through delivery, adoption, and optimization. The role requires strong product leadership, technical fluency, quantitative reasoning, stakeholder management, and the ability to drive outcomes in a matrixed organization.

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.

Supports the development, commercialization, and growth of products serving Mastercard ecosystem partners. Analyzes market, customer, and performance data; tracks KPIs and adoption metrics; identifies opportunities; and develops actionable recommendations. Partners with product, technology, analytics, and data science teams on AI-enabled capabilities, go-to-market initiatives, partner adoption strategies, dashboards, presentations, and regional ecosystem growth.

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.

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.

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.

Leads the development, launch, scaling, and continuous improvement of AI-powered products. Translates product vision into roadmaps, manages product lifecycles, prioritizes features, tracks budgets and resources, and uses adoption, performance, and value metrics to guide decisions. Partners with business owners, cross-functional teams, DDIT, and external parties to deliver products and transition them into maintenance. Ensures stakeholder alignment, user satisfaction, measurable process improvement, and ethical AI deployment.

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.

Build, deploy, operate, and architect production machine learning systems for pricing, demand forecasting, and fleet management. Partner with Data Scientists to establish reliable training, serving, monitoring, retraining, and rollback workflows. Define ML quality standards, automated checks, alerts, and platform tooling while collaborating with Product and Engineering. Use Python, SQL, cloud platforms, containers, orchestration, CI/CD, and model registries to deliver scalable systems.

Develop complex cloud applications for the Altium 365 platform. Responsibilities include building cloud-native applications and scalable infrastructure, designing APIs and data models, developing stateful and stateless services, working with relational databases and ORM frameworks, and contributing to AI-powered search and large-scale data processing. The role requires technical ownership, collaboration, solving complex challenges, influencing technical direction, and establishing engineering standards and governance.

Build complex, reliable, high-impact systems across infrastructure, machine learning, AI agents, LLM-driven tools, logistics, and B2B platforms. The role requires strong full-stack engineering expertise, data-driven decision-making, rapid experimentation, and the ability to independently solve challenging technical and business problems in a compact, autonomous team.

Owns fraud risk performance across payment flows by configuring and optimizing fraud models, rules, thresholds, and decision logic. Analyzes fraud KPIs, conducts root-cause investigations, implements risk controls, and translates risk strategy into mitigation actions. Collaborates with Machine Learning, Product, Compliance, and Operations teams to improve fraud detection precision, manage financial exposure, and meet regulatory expectations.

Leads the market’s digital, data, AI, automation, customer engagement, CRM, and commercial effectiveness agenda in a pharmaceutical environment. Defines strategy and capability roadmaps, prioritizes investments, oversees cross-functional product and transformation initiatives, manages vendors and partnerships, drives AI and automation adoption, governs delivery and compliance, and ensures measurable business value. The role also translates enterprise direction into local execution and leads multidisciplinary teams across commercial, market access, regional, and global stakeholders.

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.

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.

Build and maintain AWS cloud environments using Infrastructure as Code, automate infrastructure maintenance, monitor virtual machine and container workloads, implement cybersecurity policies, support AI and machine learning architectures, guide internal teams, and coordinate incident resolution with AWS Support.

Implement and support Microsoft Customer Insights projects: integrate data sources, configure unified profiles, build customer segmentation and behavioural analyses, develop and execute test strategies, connect Customer Insights with Dynamics and other Microsoft platforms, support Sales alongside Corporate IT/T2D, and create dashboards using Power BI.

Develop and deploy machine learning models for retrieval, reranking, indexing, search relevance, crawling, and content understanding. Build large-scale embedding and retrieval systems, evaluation pipelines, and quality metrics for agent-native search. Integrate models into high-throughput production services while balancing latency, quality, and cost. Collaborate on architecture and product decisions involving search, retrieval, and LLM-based systems.

Develop and maintain FARFETCH’s Python-based Statistical Engine powering its experimentation platform. Ensure accurate, reliable, and scalable A/B test results; introduce statistical methods and experiment designs; improve data integration and platform maintainability; and collaborate with Data Engineering, Analytics, Product, and Engineering teams. The role also explores AI-assisted development and advanced experimentation techniques such as causal inference and variance reduction.

Lead product marketing for the French market by developing localized positioning, messaging, sales enablement, launch campaigns, and customer insights. Partner cross-functionally with product, sales, marketing, engineering, design, communications, and partnerships teams. Track adoption and market performance, identify growth opportunities, and lead product marketing strategy for a major Paris customer event. The role requires French and English fluency, B2B technology marketing experience, and travel within France and internationally.

Own the end-to-end strategy, roadmap, and outcomes for a network intelligence and security product serving ISP customers. Partner with engineering, data science, security, and compliance teams on architecture, ML evaluation, privacy, and production delivery. Conduct customer discovery, analyze market and threat trends, define requirements and metrics, build business cases, and represent the product to technical customers, executives, sales, marketing, and public audiences.

Leads client-facing data and analytics consulting engagements, advising on data engineering, BI, advanced analytics, and AI solutions. Responsibilities include requirements gathering, solution architecture, Microsoft Fabric and Azure Databricks implementations, ETL/ELT design, data modelling, Power BI reporting, and delivery leadership. The role also mentors junior consultants, oversees project execution, and supports the growth of the Portugal-based Data, Analytics & AI team.

Designs and delivers data engineering, business intelligence, and advanced analytics solutions for clients. Responsibilities include gathering requirements, modeling and transforming data, developing cloud-based data platforms and ETL solutions, creating reports and dashboards, advising on analytics best practices, and integrating machine learning or generative AI capabilities. The role requires strong client-facing consulting experience and the ability to translate business needs into technical solutions.

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.