Data Scientist
TechJobs.ie Job Insights
At a glance
- Employment type
- Full-time
- Workplace
- Remote
- Salary
- €100k/year (EUR)
- Category
- Data engineering
Technologies & skills
What you'll be doing
Join a global AI leader as a Data Scientist focused on building ML models and data solutions for risk intelligence. Own end-to-end projects, collaborate with international teams, and deliver predictive insights across financial, cyber, operational, ESG, and compliance risk. Fully remote role based in Ireland.
- Build and maintain machine learning models and data pipelines
- Analyse complex datasets and develop predictive and classification models
- Improve data quality, automation, and modelling processes
- Translate business challenges into scalable technical solutions
- Deploy, monitor, and optimise models in production environments
Key requirements
Must-have
- 3-5 years of experience in Data Science or ML
- Strong Python skills
- Strong SQL, database, and data engineering fundamentals
Experience: 3-5 years
Role signals
- Technical focus
- Data Science, Machine Learning, Model Development, Data Engineering
- Hands-on vs management
- Hands-on
Similar jobs
Full job description
Data Scientist – Machine Learning & AI-Powered Risk Intelligence
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Join a global AI leader transforming risk intelligence
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Build ML models with real business impact
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Own high-impact data science projects in Ireland
An exciting opportunity has arisen for a Data Scientist to join a global technology organisation that builds AI-powered risk intelligence solutions. This hands-on role focuses on developing models, pipelines, and data solutions that support predictive insights across financial, cyber, operational, ESG, and compliance risk.
Working closely with Product, Engineering, and Data teams globally, you’ll take ownership of complex problems, build practical solutions, and continuously improve models and processes through an iterative approach.
This is a unique opportunity to play a key role in a growing data science function, where your work will directly influence product innovation, improve decision-making, and help deliver smarter risk solutions for global organisations.
Why This Role Stands Out
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Work on AI and machine learning solutions used by global enterprise clients
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Own projects end-to-end, from data preparation and modelling through to production deployment
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Join a growing Ireland team with strong visibility and impact
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Collaborate with international Product and Technology teams
What You’ll Be Doing
-
Build and maintain machine learning models and data pipelines
-
Analyse complex datasets and develop predictive and classification models
-
Improve data quality, automation, and modelling processes
-
Translate business challenges into scalable technical solutions
-
Deploy, monitor, and optimise models in production environments
What You Bring
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3-5 years of experience in Data Science or ML with strong Python skills
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Strong SQL, database, and data engineering fundamentals
Package & Benefits
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Competitive salary up to 100,000 + 10-15% Bonus annually + Equity!
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Fully remote role based in Dublin, Ireland
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Opportunity to work with a global AI-focused team
Interested in building AI solutions that solve complex business problems and deliver real-world impact?
Contact Serena Akbib on +353 1 960 9972 or send your CV to [email protected].
Interview prep pack
Grounded in this listing. Use it to prepare examples before you apply.
Your interview focus
You will be assessed on your ability to build, deploy, and optimise machine learning models for risk intelligence, using Python, SQL, and AWS. Expect to demonstrate end-to-end project ownership, strong data engineering fundamentals, and the ability to translate business problems into scalable solutions.
- Machine learning model development·High
- Data pipeline engineering·High
- Python & SQL expertise·High
- Production deployment·High
Only have 30 minutes?
Follow a focused preparation plan based on this job.
Start 30-minute prep
Your 30-minute plan
Review End-to-End ML Project Experience
0–10 minRecall specific projects where you owned the process from data prep to deployment, focusing on business impact and technical decisions.
Refresh Python, SQL, and AWS Skills
10–17 minGo over recent code or notes on using these technologies for data pipelines and model deployment.
Prepare Collaboration and Communication Examples
17–24 minThink of stories where you worked with product, engineering, or international teams to deliver data solutions.
Draft Questions for the Interviewer
24–30 minWrite down thoughtful questions about team priorities, project lifecycle, and technology choices.
Talking points
, 6 itemsMachine Learning Model Development
You will design, build, and maintain predictive and classification models that drive the company's risk intelligence solutions.
Data Pipeline Engineering
Building and maintaining robust data pipelines is essential for preparing and processing complex datasets for modelling.
Python and SQL Proficiency
Strong Python and SQL skills are explicitly required for data manipulation, model building, and database interactions.
Production Deployment and Monitoring
You will deploy, monitor, and optimise models in production environments, ensuring reliability and performance.
Business Problem Translation
Translating business challenges into scalable technical solutions is a core responsibility in this role.
Collaboration with Global Teams
You will work closely with Product, Engineering, and Data teams internationally, requiring effective communication and teamwork.
What to research
, 2 itemsEnd-to-End Project Ownership
Be ready to discuss projects where you managed the full lifecycle, from data preparation through to production deployment and monitoring.
Machine Learning for Risk Intelligence
Prepare to explain your approach to building predictive models specifically for risk domains like financial, cyber, or compliance.
Questions to ask
, 6 itemsHow does the team prioritise which risk intelligence problems to tackle next?
Why ask this? Clarifies how your work will be aligned with business priorities.
What are the main challenges the data science team faces when deploying models into production?
Why ask this? Reveals technical hurdles and expectations for deployment.
How is success measured for data science projects in this organisation?
Why ask this? Helps you understand performance metrics and impact.
What opportunities exist for collaboration with international teams?
Why ask this? Shows how cross-functional and global the work environment is.
How does the company support ongoing learning and development in AI and ML?
Why ask this? Indicates commitment to your professional growth.
What is the typical lifecycle of a data science project here?
Why ask this? Gives insight into project ownership and pace.
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