Data Scientist – Data Preparation & Insight Delivery

CPL · Recruitment agency

Full-time•Data engineering•Dublin, Ireland · Remote within Ireland · Hybrid

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At a glance

Employment type: Full-time
Location: Dublin, Ireland · Remote within Ireland · Hybrid
Workplace: Hybrid
Category: Data engineering

Technologies & skills

Primary technologies

Other technical skills

What you'll be doing

Join a global cybersecurity organization in Dublin as a Data Scientist focused on data preparation and insight delivery. Work with varied data sources, develop analyses and models, and collaborate with engineering, AI/ML, and business teams. Hybrid role with three days/week in-office.

Acquire, clean, transform and reconcile data from CRM, operational and platform-related systems
Develop exploratory analysis, metrics, models and visualisations to identify trends, anomalies and risks
Support ongoing management and quality control of systems and CRM data
Document data definitions, transformations, assumptions and analytical processes
Validate platform data flows and outputs, record defects and support user acceptance testing
Work closely with engineering, AI/ML, security, operations and business stakeholders

Key requirements

Must-have

2+ years experience in a similar role
Relevant Degree or Masters
Strong practical experience in data science, analytics or insight delivery
Advanced SQL and Python skills
Experience working with CRM, operational or multi-source datasets
Strong data preparation, modelling, validation and visualisation capability
Ability to turn ambiguous business questions into structured analysis and actionable recommendations
Irish/EU Citizenship

Nice-to-have

Experience with BI tools
Experience with cybersecurity or risk-related data

Experience: 2+ years

Role signals

Technical focus: data science, analytics, data preparation, modelling, visualisation
Hands-on vs management: Hands-on

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Full job description

Data Scientist – Data Preparation & Insight Delivery – Dublin

Cpl are partnering with a global cybersecurity organisation that is expanding its data and technology capability in Dublin. This opportunity suits a practical Data Scientist who enjoys working with varied data sources, identifying insights and validating data-led solutions.
The role is hybrid, requiring an average of three days per week in the Dublin office.

Responsibilities

Acquire, clean, transform and reconcile data from CRM, operational and platform-related systems.
Develop exploratory analysis, metrics, models and visualisations to identify trends, anomalies and risks.
Support the ongoing management and quality control of systems and CRM data.
Document data definitions, transformations, assumptions and analytical processes.
Validate platform data flows and outputs, recording defects and supporting user acceptance testing.
Work closely with engineering, AI/ML, security, operations and business stakeholders.

Requirements

2+ Years experience in a similar role.
Relevant Degree or Masters essential
Strong practical experience in data science, analytics or insight delivery.
Advanced SQL and Python skills.
Experience working with CRM, operational or multi-source datasets.
Strong data preparation, modelling, validation and visualisation capability.
Ability to turn ambiguous business questions into structured analysis and actionable recommendations.
Experience with BI tools and cybersecurity or risk-related data would be advantageous.
Candidates must have Irish/EU Citizenship
Must currently live within a commutable distance of Dublin.

On Offer

An opportunity to work on varied, high-value data challenges within cybersecurity.
A collaborative environment alongside engineering, AI and security specialists.
Hybrid Dublin working and strong potential to grow with a newly established Irish team.
#LI-JM2

Interview prep pack

Grounded in this listing. Use it to prepare examples before you apply.

Your interview focus

Based on this listing, the role focuses on hands-on data preparation, analysis, and insight delivery within a cybersecurity context, requiring strong SQL and Python skills and experience with multi-source datasets.

Hands-on data preparation and analysis·High
Advanced SQL and Python proficiency·High
Collaboration with technical and business teams·Medium

Only have 30 minutes?

Follow a focused preparation plan based on this job.

Start 30-minute prep

Your 30-minute plan

Review and Practice Advanced SQL and Python
0–10 min
Spend time on hands-on exercises involving data cleaning, transformation, and analysis using SQL and Python.
Prepare STAR Stories for Data Projects
10–17 min
Outline 2-3 examples from your experience that demonstrate data preparation, validation, and insight delivery.
Research CRM and Operational Data Models
17–22 min
Read up on typical CRM and operational data structures to relate your experience to the role's requirements.
Review BI Tools and Visualisation Examples
22–27 min
Select and rehearse examples where you used BI tools to communicate insights to stakeholders.
Draft Role-Specific Questions
27–30 min
Prepare thoughtful questions to ask the interviewers about the team, data challenges, and growth opportunities.

Likely questions

Expand all

Priority reflects how strongly this topic is emphasised in the job listing, not whether it will be asked.

1Describe a project where you acquired and reconciled data from multiple sources, such as CRM and operational systems.High
Why they may ask
Assesses your practical experience with data integration and cleaning, which is central to the role.
Prepare to discuss
- Clearly outline the data sources and challenges faced.
- Explain the tools and methods used for cleaning and reconciliation.
- Discuss how you ensured data quality and consistency.
- Share the impact of your work on the project outcome.
Tip: Use a STAR (Situation, Task, Action, Result) format to structure your answer.
2How do you approach exploratory data analysis to identify trends, anomalies, or risks?High
Why they may ask
Evaluates your analytical thinking and ability to generate insights from complex datasets.
Prepare to discuss
- Describe your typical EDA workflow and tools (e.g., Python libraries, SQL).
- Give an example of a trend or anomaly you uncovered.
- Explain how you validated your findings.
- Discuss how your insights informed business decisions.
Tip: Be specific about techniques and visualisation tools you use.
3What steps do you take to validate data flows and support user acceptance testing?Medium
Why they may ask
Tests your attention to detail and experience with data validation and quality assurance.
Prepare to discuss
- Outline your process for validating data pipelines and outputs.
- Mention tools or frameworks you use for testing.
- Describe how you document defects and communicate with stakeholders.
- Share an example of resolving a data quality issue.
Tip: Highlight both technical and communication aspects.
4Can you give an example of turning an ambiguous business question into a structured analysis?High
Why they may ask
Assesses your problem-solving skills and ability to deliver actionable recommendations.
Prepare to discuss
- Describe the initial ambiguity and how you clarified requirements.
- Explain your approach to structuring the analysis.
- Discuss the analytical methods and tools used.
- Share the outcome and business impact.
Tip: Emphasise communication with stakeholders and iterative refinement.
5How have you documented data definitions, transformations, and analytical processes in past projects?Medium
Why they may ask
Checks your ability to maintain transparency and reproducibility in data science work.
Prepare to discuss
- Explain your documentation practices and tools.
- Discuss the importance of clear documentation for collaboration.
- Provide an example where documentation prevented issues or aided onboarding.
Tip: Mention any standards or templates you follow.
6What experience do you have working with BI tools and visualising data for non-technical stakeholders?Medium
Why they may ask
Assesses your ability to communicate insights effectively to diverse audiences.
Prepare to discuss
- List BI tools you have used (e.g., Tableau, Power BI).
- Describe a time you tailored visualisations for business users.
- Explain how you ensured clarity and actionable takeaways.
Tip: Focus on the impact of your visualisations.
7Describe a situation where you collaborated with engineering, AI/ML, or security teams on a data project.Medium
Why they may ask
Evaluates your teamwork and cross-functional communication skills.
Prepare to discuss
- Set the context and teams involved.
- Explain your role and contributions.
- Discuss challenges and how you resolved them.
- Highlight the project outcome.
Tip: Show openness to feedback and adaptability.

Talking points

Data Preparation and Cleaning
Demonstrate your ability to handle messy, multi-source data and ensure data quality for analysis.
Exploratory Data Analysis and Visualisation
Showcase your skills in identifying trends, anomalies, and actionable insights using Python, SQL, and BI tools.
Data Validation and Quality Control
Highlight your experience in validating data flows, reconciling datasets, and supporting user acceptance testing.
Turning Business Questions into Analysis
Illustrate how you translate ambiguous requirements into structured, actionable recommendations.
Collaboration with Stakeholders
Provide examples of working with engineering, AI/ML, security, and business teams to deliver data-driven solutions.

What to research

Review Advanced SQL and Python Techniques
Refresh your knowledge of data manipulation, cleaning, and analysis using SQL and Python, as these are core requirements.
Understand CRM and Operational Data Structures
Familiarise yourself with common CRM and operational data models, as the role involves integrating and analysing such datasets.
Prepare Examples of Data Validation and Quality Control
Gather concrete examples from your experience where you validated data flows, reconciled datasets, or supported user acceptance testing.
Explore BI Tools for Data Visualisation
Review your experience with BI tools and prepare to discuss how you have used them to communicate insights.

Questions to ask

What are the main data sources and platforms I would be working with in this role?
Why ask this? Clarifies the technical environment and helps you relate your experience to their stack.
How does the team currently approach data quality control and validation?
Why ask this? Shows your interest in best practices and readiness to contribute to process improvement.
What are the most common business questions or challenges the data science team addresses?
Why ask this? Helps you understand the business context and tailor your examples.
How does the data science team collaborate with engineering, AI/ML, and security teams?
Why ask this? Demonstrates your interest in cross-functional teamwork and communication.
What opportunities are there for professional growth as the Irish team expands?
Why ask this? Shows your long-term interest and commitment to the organisation.
What are the biggest data challenges the team is currently facing?
Why ask this? Gives insight into immediate priorities and how you can add value.

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Apply NowApply before: 5 Oct 2026