Lead Data Scientist – Remote, Scale-Up
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At a glance
- Employment type
- Full-time
- Location
- Ireland · Remote within Ireland
- Workplace
- Remote
- Category
- Data engineering
Technologies & skills
Cloud & infrastructure
What you'll be doing
Lead Data Scientist role at a SaaS scale-up, joining a new R&D team to build greenfield data science solutions. Hands-on position with technical leadership responsibilities, designing and productionising ML/AI features. Fully remote in Ireland, with occasional Dublin meetings. High-impact, growth-focused opportunity.
Key requirements
Must-have
- Solid experience as a Senior Data Scientist or Lead Data Scientist
- Experience providing technical leadership
- Strong experience designing and productionising data science, ML, and AI solutions
- Experience building cloud-based data solutions
Role signals
- Technical focus
- data science, machine learning, AI, MLOps, predictive modelling
- Leadership
- Mentoring
- Architecture / system design
- Indicated in the listing
- Hands-on vs management
- Hands-on
Similar jobs
Full job description
Lead Data Scientist – Remote, Scale-Up
Lead Data Scientist opportunity with a SaaS Scale-Up in Dublin – in this role you will join a newly founded R&D team building green field data science solutions for customers globally. This is a fully remote team who meet once or twice a month in Dublin.
Newly created role where you will lead the creation of green field data science features in a successful established product. This is a hands on role where you will design & then build Machine Learning, AI and Data Science solutions yourself, and provide technical leadership to a team of more junior data scientists.
This is a highly visible opportunity, with the backing of senior leadership in a large, global organization. There is huge scope to make an impact in the business and grow within this role.
The team is high calibre, with a great culture. They are energetic, positive, passionate people. You will work closely with the senior leadership team, including the CTO.
The ideal candidate will be a strong, experienced Data Scientist, who enjoys technical leadership & contributing as a hands on data scientist. Strong experience productionising solutions as well as designing models is essential.
Role / opportunity:
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Data Science Lead / Lead Data Scientist
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Green field data science features for product with huge potential
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ScaleUp SaaS Company
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Direct path to grow in seniority, build a larger team, and make an impact in the organisation
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Build a green field, cloud native data science platform
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Python, AWS, Kubernetes, MLOps, Predictive Modelling, AI, Machine Learning
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No shortage of challenging technical problems to solve
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Autonomous, senior level role with opportunity to make a significant impact
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Work closely with department heads and CTO
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Nice culture – high pace, creative, collaborative
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Huge growth and progression opportunities
Skills / experience:
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Solid experience as a Senior Data Scientist or Lead Data Scientist with experience providing technical leadership
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Strong experience both designing and productionising data science, ML and AI solutions.
-
Experience building cloud based data solutions ideally
Salary:
Strong compensation on offer depending on experience
For more information contact Kieran Tumulty in confidence on 01 649 8510 or [email protected]
Interview prep pack
Grounded in this listing. Use it to prepare examples before you apply.
Your interview focus
Based on this listing, the Lead Data Scientist role is a hands-on, senior-level position at a SaaS scale-up, focused on designing and productionising greenfield data science and AI solutions while providing technical leadership to a growing team.
- Hands-on technical leadership·High
- Cloud-native data science·High
- Productionising ML/AI solutions·High
- Collaboration with senior leadership·Medium
Only have 30 minutes?
Follow a focused preparation plan based on this job.
Start 30-minute prep
Your 30-minute plan
Review and Select Key Leadership and Technical Projects
0–8 minIdentify 2-3 projects that best demonstrate your experience leading teams and productionising ML/AI solutions, focusing on those relevant to cloud environments.
Refresh Knowledge of Cloud-Native Tools and MLOps
8–15 minReview your experience with AWS, Kubernetes, and MLOps workflows, and be ready to discuss specific tools and practices you have used.
Prepare Stakeholder Communication Examples
15–20 minThink through examples where you worked with senior leadership or cross-functional teams, emphasizing your ability to align technical work with business goals.
Draft Questions for the Interviewers
20–25 minPrepare thoughtful questions about the team, technical challenges, and company culture to demonstrate your engagement and leadership mindset.
Practice Articulating Your Approach to Greenfield Projects
25–30 minBe ready to explain how you handle ambiguity, set direction, and deliver results in new and evolving environments.
Talking points
, 5 itemsTechnical Leadership in Data Science Teams
You will be expected to lead and mentor junior data scientists, so prepare examples of how you have guided teams, set technical direction, or fostered collaboration.
End-to-End Machine Learning Solution Development
The role emphasizes both designing and productionising ML/AI solutions; be ready to discuss projects where you took models from concept to deployment.
Cloud-Native and MLOps Practices
Experience with AWS, Kubernetes, and MLOps is highlighted; prepare to explain how you have built or managed cloud-based data science platforms.
Stakeholder Engagement and Cross-Functional Collaboration
You will work closely with senior leadership, including the CTO, so be prepared to share how you communicate technical concepts and align data science initiatives with business goals.
Greenfield Project Experience
The role involves building new features and platforms from scratch; discuss your approach to greenfield projects and handling ambiguity.
What to research
, 4 itemsRecent Projects in Productionising ML/AI Solutions
Review your experience taking machine learning models from design to deployment, especially in cloud environments.
Technical Leadership Examples
Prepare stories that showcase your ability to mentor, lead, and set technical direction for data science teams.
Cloud-Native Data Science Tools
Refresh your knowledge of AWS, Kubernetes, and MLOps practices relevant to building scalable data science platforms.
Stakeholder Communication
Think of examples where you worked closely with senior leadership or cross-functional teams to deliver impactful data science solutions.
Questions to ask
, 6 itemsWhat are the immediate priorities for the new R&D team and the greenfield data science platform?
Why ask this? Clarifies expectations and helps you understand where you can make the most impact early on.
How is success measured for this role and the data science initiatives?
Why ask this? Ensures alignment with leadership’s vision and helps you tailor your approach to key metrics.
What is the current team structure and what are the plans for growth?
Why ask this? Gives insight into your leadership responsibilities and future opportunities for team building.
How does the data science team collaborate with product and engineering teams?
Why ask this? Helps you understand cross-functional workflows and integration points.
What are the biggest technical challenges the team is currently facing?
Why ask this? Allows you to assess where your expertise can add value and prepare for potential obstacles.
How often does the remote team meet in person, and what is the purpose of these meetings?
Why ask this? Clarifies expectations around remote work and team culture.
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