Don’t send a generic resume — generate a resume and cover letter tailored to this exact role.
Novartis in Dublin is seeking a senior professional to co-lead AI and automation adoption within the Advanced Quantitative Sciences Technology & AI function. You will translate business needs into technical requirements, collaborate with engineering and platform teams, and drive scalable, compliant changes in clinical data workflows.
Role emphasizes change leadership, stakeholder engagement, and hands-on prototyping to accelerate adoption from pilot to standard practice while staying aligned
Salary Range:
€89,180.00 - €165,620.00
Job Description Summary
#LI-Hybrid
Location: Dublin, Ireland
This role is based in Dublin, Ireland. Novartis is unable to offer relocation support for this role. Please only apply if this location is accessible for you.
Complex data are integral to our work in clinical studies. This position is part of the Advanced Quantitative Sciences (AQS) Technology & AI function at Novartis. Our mission is to use automation and artificial intelligence to unlock the potential of complex scientific data, speed up quantitative decision-making in clinical trials, and bring innovative medicines to patients faster.
Our new Statistical Computing Environment (SCE) is changing how AQS works, from how programming and QC are done in the SCE to how outputs flow across the AQS Digital workflow. The AI & Automation Catalyst turns that technology into lasting changes in daily practice. You will co-lead, with engineering and business partners, the rollout of AI and automation into the how we generate evidence. That covers redesigning processes, being a technical champion, bringing stakeholders along, and making sure new ways of working are adopted, compliant and deliver measurable value.
This is a technical role and a change role, not one or the other. You are credible enough with engineers and programmers to shape solutions and challenge designs. You are also skilled enough with people to move senior stakeholders, line functions and skeptical end users from pilot to standard practice.
At Novartis, we’re committed to reimagining medicine together - and rewarding the people who make it happen.
The base salary offered is determined based on gender-neutral objectives, such as relevant skills, competencies and experience in accordance with the Novartis pay setting policy and upon joining Novartis will be reviewed periodically.
In addition to your base salary, you may be eligible for a performance-based bonus depending on certain performance parameters.
The rewards of being part of our team go far beyond base pay and incentives. We also offer a variety of competitive benefits in kind to help you thrive personally and professionally, such as insurance plans, retirement plans, wellbeing resources and global recognition programs. In addition, we provide flexible and hybrid working options, where possible, and minimum 14 weeks paid parental leave.
Pay equity is a fundamental principle of our employment policy and reflects our commitment to create a diverse, equitable and inclusive environment that treats all employees with dignity and respect, as outlined in our Code of Ethics.
Read our brochure to learn more about our global total rewards offering:
https://www.novartis.com/sites/novartis_com/files/novartis-life-handbook.pdf
Note: Benefits and compensation may vary by country and are subject to local legal requirements, including provisions of collective bargaining agreements where applicable. A full overview of your compensation package, including any relevant collective bargaining agreement details applicable to your role based on your employment location and Novartis employer entity, will be communicated separately to you during the application process.
Novartis is committed to building an outstanding, inclusive work environment and diverse teams’ representative of the patients and communities we serve.
Artificial Intelligence (AI), Biostatistics, Business Value Creation, Change Management, Curious Mindset, Data Governance, Data Literacy, Data Quality, Data Science, Data Visualization, Deep Learning, Graph Algorithms, Learning Agility, Machine Learning (ML), Machine Learning Algorithms, Python (Programming Language), Stakeholder Engagement, Statistical Analysis, Time Series Analysis