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Data Scientist
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At a glance
- Employment type
- Contract
- Workplace
- Remote
- Day rate
- €500-600/day (EUR)
- Category
- Data engineering
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
Review End-to-End ML Project
0–10 minSelect a recent project where you led model development and deployment. Note challenges, business context, and outcomes.
Refresh Python, SQL, and dbt Skills
10–16 minSkim key scripts or notebooks showing advanced data manipulation and transformation. Review dbt documentation if needed.
Prepare Communication Examples
16–24 minRecall times you explained technical concepts to non-technical stakeholders. Focus on clarity and business relevance.
Reflect on Ambiguity Handling
24–30 minIdentify examples where you structured open-ended problems and defined success metrics with imperfect data.
Talking points
, 6 itemsEnd-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 itemsEnd-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 itemsHow 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.
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.
How are priorities managed across multiple projects and stakeholders?
Why ask this? Reveals how workload and tradeoffs are handled.
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.
How does the team handle ambiguity or incomplete requirements in new projects?
Why ask this? Indicates support structures and expectations for independent problem-solving.
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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