Data Scientist

CPL · Recruitment agency
ContractData engineering€500-600/day (EUR)Dublin, Ireland · Remote within Ireland
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At a glance

Employment type
Contract
Workplace
Remote
Day rate
€500-600/day (EUR)

Technologies & skills

Primary technologies

Other technical skills

What you'll be doing

A Senior Data Scientist is needed for a contract role within the Data Science & Analytics team, focusing on developing data products and machine learning solutions to enhance customer understanding and go-to-market decisions. The role requires strong technical judgment and the ability to work with complex datasets.

  • Design, develop, validate, and improve data science models and products.
  • Define customer and business value metrics, evaluation approaches, and validation criteria for models and products.
  • Use SQL to transform complex data and build scalable datasets for modeling and analysis.
  • Collaborate with product owners and stakeholders to shape new data product ideas or enhancements.
  • Develop technical design and architecture documentation for data science and AI products.
  • Manage priorities across multiple projects.

Key requirements

Must-have

  • 5+ years of experience in data science, computer science, statistics, or a related quantitative field.
  • Advanced Python and SQL skills.
  • Experience leading end-to-end development of machine learning models.

Nice-to-have

  • Experience with dbt.
  • Familiarity with go-to-market data such as Salesforce.
  • Experience using AI productivity and coding tools.

Experience: 5+ years

Role signals

Technical focus
Data engineering
Architecture / system design
Indicated in the listing
Hands-on vs management
Mixed

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Full job description

Senior Data Scientist

Day Rate Contract - €500- 600 per day

Remote within Ireland

Position Overview

A Senior Data Scientist is required to join the Data Science & Analytics team within our Go-to-Market Data Intelligence organization. This team develops data products, predictive models, machine learning products, and applied AI solutions that help better understand customers and improve go-to-market decisions across the customer lifecycle.

In this role, you will apply machine learning, statistical methods, and practical AI techniques to complex business problems with meaningful impact. You will work across complex, real-world datasets, develop and improve models, and partner with cross-functional stakeholders to turn data into scalable products and business insights. This role is well suited for a strong individual contributor who brings deep technical judgment, thrives in ambiguity, and can guide high-impact work across multiple efforts.

Responsibilities

  • Design, develop, validate, and improve data science models and products. Participate in stakeholder planning sessions to gather context on ambiguous business challenges, and recommend data science modeling approaches that can drive business impact
  • Define customer and business value metrics, evaluation approaches, and validation criteria for models and products, including in situations with imperfect or incomplete data
  • Use SQL to transform complex data and build scalable datasets for modeling and analysis
  • Work with the machine learning engineering team to develop, validate, and deploy machine learning and AI products
  • Collaborate with product owners and other stakeholders to shape new data product ideas or enhancements
  • Develop technical design and architecture documentation for data science and AI products
  • Help drive technical direction, working alongside engineering and product owners to navigate tradeoffs and execution decisions
  • Manage priorities across multiple projects, including helping shape tradeoffs and cross-functional team priorities
  • Become an expert in the organisations data - recommend approaches to make reusable features and data products that can be leveraged to ensure data science products are built on trusted, stable and scalable foundations.
  • Communicate analyses, model performance, methodology, and recommendations clearly to technical and non-technical audiences

Minimum Qualifications

  • 5+ years of experience and a graduate degree in data science, computer science, statistics, or a related quantitative field
  • Advanced Python and SQL skills, including experience working with large-scale data warehouses
  • Experience leading end-to-end development of machine learning models, data science products, or advanced analytical solutions in a business environment
  • Strong communication skills and the ability to explain technical concepts, decisions, and tradeoffs to different audiences

Preferred Qualifications

  • Experience with dbt

  • Familiarity with go-to-market data such as Salesforce, financial metrics, or product usage and engagement data

  • Experience using AI productivity and coding tools
    The Ideal Candidate

  • Can tell compelling stories with data and connect analysis to what is happening in the business

  • Is a strong communicator who can adapt to technical and non-technical audiences

  • Is flexible and works effectively with their manager, product owners, and stakeholders to prioritize across multiple projects

  • Has strong attention to detail and cares deeply about data quality

  • Proactively engages stakeholders to better understand business needs

  • Enjoys collaborating with team members to drive impact

  • Is comfortable working through ambiguity and bringing structure to open-ended problems

  • Can define what success looks like, even when data, requirements, or conditions are imperfect
    For more info please call Michael on 01-6146058 or e: michael.fitzgerald@cpl.ie

#LI-MF7

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 design, develop, and deploy machine learning models and data products using Python, SQL, and dbt. Strong communication, stakeholder collaboration, and experience with ambiguous business problems are essential. Expect to demonstrate technical depth and business impact.

  • Machine learning model development·High
  • Python & SQL expertise·High
  • Stakeholder collaboration·High
  • Business impact analysis·High

Only have 30 minutes?

Follow a focused preparation plan based on this job.

Start 30-minute prep

Your 30-minute plan

  1. Review End-to-End ML Project

    0–10 min

    Select a recent project where you led model development and deployment. Note challenges, business context, and outcomes.

  2. Refresh Python, SQL, and dbt Skills

    10–16 min

    Skim key scripts or notebooks showing advanced data manipulation and transformation. Review dbt documentation if needed.

  3. Prepare Communication Examples

    16–24 min

    Recall times you explained technical concepts to non-technical stakeholders. Focus on clarity and business relevance.

  4. Reflect on Ambiguity Handling

    24–30 min

    Identify examples where you structured open-ended problems and defined success metrics with imperfect data.

Likely questions

, 8 items

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

Talking points

, 6 items
  • End-to-End Machine Learning Model Development

    You will be expected to lead the full lifecycle of model development, from problem definition to deployment and validation, often in ambiguous situations.

  • Advanced Python and SQL for Data Science

    The role requires advanced skills in Python and SQL to manipulate large datasets and build scalable data pipelines.

  • Data Product Design and Documentation

    You will design and document data science and AI products, ensuring they are robust, scalable, and reusable.

  • Stakeholder Engagement and Communication

    You must clearly explain technical concepts and recommendations to both technical and non-technical audiences, and gather requirements from stakeholders.

  • Handling Imperfect or Incomplete Data

    You will often work with real-world data that is messy or incomplete, and must define metrics and validation criteria under these conditions.

  • Go-to-Market Data and Business Impact

    Understanding go-to-market data and connecting data science work to business outcomes is central to the team’s mission.

What to research

, 4 items
  • End-to-End Model Development and Deployment

    Expect deep questions on leading the full lifecycle of machine learning models, from design to deployment and validation, especially in ambiguous business contexts.

  • Advanced Python and SQL for Large-Scale Data

    You will need to demonstrate advanced skills in Python and SQL, including building scalable datasets and working with large data warehouses.

  • Communicating Technical Concepts to Stakeholders

    Strong communication with both technical and non-technical audiences is emphasized, so prepare to show how you adapt your messaging and drive business impact.

  • Defining Metrics and Validation with Imperfect Data

    You will be expected to set evaluation criteria and success metrics, even when data is incomplete or messy, and explain your approach.

Questions to ask

, 6 items
  1. How does the team define and measure the business impact of data science products?

    Why ask this? Clarifies expectations for connecting technical work to business outcomes.

  2. What are the main types of go-to-market data the team works with?

    Why ask this? Helps you assess your fit with the data sources and domain.

  3. How are priorities managed across multiple projects and stakeholders?

    Why ask this? Reveals how workload and tradeoffs are handled.

  4. What is the typical process for deploying machine learning models into production?

    Why ask this? Shows the maturity of deployment practices and your potential role.

  5. How does the team handle ambiguity or incomplete requirements in new projects?

    Why ask this? Indicates support structures and expectations for independent problem-solving.

  6. What opportunities exist for shaping the technical direction of data science products?

    Why ask this? Clarifies your influence on technical decisions and growth.

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Apply NowApply before: 24 Sep 2026