Data Scientist - Applied AI

Roche; Madrid; Híbrido; EUR 60.000 - 90.000; Jornada completa

Descripción de la vacante

Roche is seeking a Data Scientist in Madrid to shape AI and data-driven solutions within the Partnering Digital Solutions team. You will translate business challenges into scalable models, collaborate with data engineers, product owners, and stakeholders, and contribute to operational intelligence across partnering activities.

The role blends applied AI with ML and requires occasional late-afternoon availability, offering a hybrid setup with two in-office days to drive impactful digital

Responsabilidades

• Design, develop, and deploy ML/AI-enabled solutions for partnering activities.
• Collaborate with product owners, data engineers, and stakeholders to translate business needs into analytics.
• Lead exploratory data analysis, feature engineering, model evaluation, and deployment.
• Support operational intelligence and decision making with dashboards and KPIs.

Descripción del empleo

At Roche you can show up as yourself, embraced for the unique qualities you bring. Our culture encourages personal expression, open dialogue, and genuine connections, where you are valued, accepted and respected for who you are, allowing you to thrive both personally and professionally. This is how we aim to prevent, stop and cure diseases and ensure everyone has access to healthcare today and for generations to come. Join Roche, where every voice matters.

Data Scientist - Applied AI

At Roche Digital Technology (RDT), innovation meets purpose. As a global community of business‑minded technologists, we are shaping the future of digital healthcare. Our mission is to power Roche through cutting‑edge technologies—harnessing artificial intelligence, data, and scalable tech innovations. Driven by passion, we build digital solutions that enable smarter ways of working, unlock human potential, and drive meaningful breakthroughs for patients worldwide.

Work Environment & Schedule

We balance flexibility with in‑person connection, averaging two days per week in the office for workshops, town halls, team meetings, and collaborative events. Because we operate across global time zones, this role requires frequent availability during late afternoons and evenings.

About Our Team: Partnering Digital Solutions

Join our Partnering Digital Solutions team, a strategic group focused on maximizing partnership impact through digital and AI‑enabled ecosystems to advance the Pharma and DIA Partnering business. We are at the forefront of innovation—building scalable data infrastructure, applying advanced data engineering practices, and integrating cutting‑edge external capabilities to accelerate decision‑making and unlock business value. Our work spans across multiple high‑impact initiatives, from developing intelligent data products to enabling end‑to‑end AI workflows. Whether it’s structuring complex data ecosystems or collaborating with external research institutions, this is a unique opportunity to help shape the future of data and AI at Roche by directly supporting key strategic decisions across our global Partnering organization.

Responsibilities

As a Data Scientist, you will play a critical role in shaping and delivering AI and machine learning capabilities across the Partnering Digital Solutions ecosystem. You will work closely with product owners, data engineers, architects, and business stakeholders to develop scalable machine learning models, intelligent workflows, and data‑driven solutions that improve visibility, prioritization, operational efficiency, and strategic decision making. This role focuses on applied AI, machine learning, predictive modelling, and operational intelligence rather than purely research‑oriented AI. You will help translate complex business challenges into scalable AI and analytics solutions that create measurable business impact across partnering operations.

Machine Learning & Applied AI Solutions

• Design, develop, and deploy machine learning and AI‑enabled solutions that support partnering activities such as opportunity prioritization, portfolio intelligence, forecasting, operational insights, and decision support
• Apply advanced analytics, statistical modelling, machine learning, and emerging AI techniques to solve complex business problems and deliver scalable intelligence capabilities across the partnering ecosystem.

Model Development & Experimentation

• Lead exploratory data analysis, feature engineering, model selection, training, validation, and performance evaluation for machine learning and AI‑enabled solutions.
• Design and evaluate multiple modelling approaches, establish appropriate evaluation metrics, and optimise models for scalability, reliability, and business impact.

Business Problem Solving & Decision Support

• Partner closely with business stakeholders and product teams to translate complex business challenges into analytical approaches, machine learning solutions, and scalable intelligence capabilities.
• Support data‑driven prioritisation and strategic decision making through actionable insights, predictive models, and operational intelligence.

AI & Intelligence Enablement

• Collaborate with AI, data, and engineering teams to operationalise AI‑enabled capabilities and support the adoption of scalable intelligence solutions across partnering platforms and workflows.
• Evaluate and apply modern AI techniques including predictive modelling, NLP, LLM‑enabled workflows, and intelligent automation approaches to enhance partnering operations and decision making.

Data & Intelligence Development

• Design and develop analytics solutions, dashboards, KPIs, and intelligence capabilities that improve visibility into partnering operations, opportunities, and portfolio activities
• Support development of operational intelligence capabilities that enable proactive and informed business decisions.

AI Operationalization & MLOps

• Collaborate with engineering and architecture teams to support operationalisation of machine learning and AI‑enabled solutions in production environments.
• Support scalable deployment, monitoring, observability, and lifecycle management of AI and machine learning capabilities aligned with enterprise AI standards