Salary at every level, the technical track versus management, and what to build next.
Typical years are a guide, not a rule. Impact matters more than tenure.
Salary ranges reflect the Ireland market in 2026. Ranges widen at senior levels because company size and equity vary significantly.
The senior technical path in data science leans toward deep specialisation: causal inference, large-scale experimentation, applied NLP, or forecasting. Companies like LinkedIn, Adobe, and Workday have built proper research tracks in their Dublin offices where individual contributors own a methodology domain rather than a business area. Outside those organisations, the senior technical track is less defined and often carries an informal management component.
Senior Data Scientist to Lead to Head of Data Science to VP Analytics. The transition to Head of Data Science is competitive. Most companies expect 8 to 12 years of experience and a track record of building teams, not just building models. The data scientists who make the jump fastest are typically the ones who ran the stakeholder readouts, not just the notebooks.
Companies actively hiring for this role in Ireland right now.
Moving into ML Engineering requires production engineering skills — Docker, CI/CD, model serving infrastructure. Moving into Data Engineering requires deeper pipeline tooling knowledge.
Most data scientists reach Senior between years 4 and 7. The companies that promote fastest are looking for people who have shipped a model into production and can point to a measurable business outcome, not just a notebook with good metrics.
It exists, but mainly at larger organisations. LinkedIn, Adobe, and Workday have defined senior individual contributor research tracks in Dublin. At most companies under 150 employees, the Lead title carries management responsibility alongside technical work. If staying technical matters to you, ask specifically what the Lead role involves before accepting.
ML Engineers earn around 30% more than Data Scientists at the median (€108k vs €83k). The gap reflects the higher engineering bar of ML Engineering roles. If salary is the primary driver, the ML Engineering transition is worth serious consideration for data scientists with software engineering fundamentals.
It depends on what part of the work you find most engaging. If productionising models, building serving infrastructure, and thinking about reliability excites you, ML Engineering is the right move. If experimentation, statistical analysis, and business insight are what you value, staying in data science is the better path. The pay ceiling for ML Engineering is higher, but the day-to-day is fundamentally different.