Data Scientist
TechJobs.ie Job Insights
At a glance
- Employment type
- Contract
- Workplace
- Remote
- Day rate
- €500-600/day (EUR)
- Category
- Data engineering
What you'll be doing
Senior Data Scientist (contract) for Go-to-Market Data Intelligence team, developing data products, predictive models, and AI solutions to improve customer understanding and business decisions. Role involves end-to-end model development, stakeholder collaboration, and technical leadership. Remote within Ireland.
- Design, develop, validate, and improve data science models and products
- Participate in stakeholder planning sessions and recommend modeling approaches
- Define customer and business value metrics, evaluation approaches, and validation criteria
- Use SQL to transform data and build scalable datasets
- Work with machine learning engineering to develop, validate, and deploy products
- Collaborate with product owners and stakeholders on data product ideas
- Develop technical design and architecture documentation
- Help drive technical direction and navigate tradeoffs
Key requirements
Must-have
- 5+ years of experience and a graduate degree in data science, computer science, statistics, or related field
- Advanced Python and SQL skills
- Experience with large-scale data warehouses
- Experience leading end-to-end development of machine learning models or data science products
- Strong communication skills
Nice-to-have
- 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
Experience: 5+ years
Role signals
- Technical focus
- data science, machine learning, AI products
- Architecture / system design
- Indicated in the listing
- Hands-on vs management
- Hands-on
Similar jobs
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
Based on this listing, the Senior Data Scientist contract role focuses on designing, developing, and deploying machine learning and AI products to drive business impact, with a strong emphasis on technical expertise, stakeholder collaboration, and communication.
- Technical depth in ML and data engineering·High
- Stakeholder communication·High
- Ability to work through ambiguity·High
- Hands-on coding and data transformation·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 Project Examples
0–8 minSelect 1-2 recent projects where you led the full lifecycle of a machine learning model, and prepare concise summaries highlighting business impact and technical decisions.
Refresh Advanced SQL and Python Skills
8–15 minRevisit code samples or notebooks demonstrating complex data transformations, feature engineering, and performance optimization.
Prepare Communication and Stakeholder Stories
15–20 minIdentify examples where you explained technical concepts to non-technical audiences or collaborated with cross-functional teams.
Gather Documentation and Metrics Examples
20–25 minCollect or outline samples of technical documentation and describe your approach to defining and validating success metrics.
Research the Organization and Team Focus
25–30 minReview any available information about the company's go-to-market strategy and data science initiatives to tailor your questions and examples.
Talking points
, 6 itemsEnd-to-End Model Development
You will need to demonstrate experience leading the full lifecycle of machine learning models, from problem definition to deployment and validation, as this is central to the role.
Advanced Python and SQL Skills
The role requires advanced coding skills for data transformation, analysis, and building scalable datasets, so be ready to discuss specific technical challenges and solutions.
Communicating Technical Concepts
Strong communication with both technical and non-technical stakeholders is emphasized, so prepare examples of translating complex analyses into actionable business insights.
Defining Metrics and Validation Criteria
You will be expected to define and justify metrics and validation approaches for models, especially in ambiguous or imperfect data situations.
Collaboration with Cross-Functional Teams
The role involves working closely with product owners, engineering, and other stakeholders, so be ready to discuss how you have navigated priorities and tradeoffs in multi-team environments.
Technical Documentation and Architecture
You will be responsible for technical design and architecture documentation, so prepare to discuss your approach to documenting and communicating technical decisions.
What to research
, 5 itemsRecent End-to-End Machine Learning Projects
Review your experience leading the full lifecycle of machine learning models, including business context, technical approach, and outcomes.
Advanced SQL and Python Use Cases
Prepare examples of complex data transformations, feature engineering, and performance optimization using SQL and Python.
Defining and Communicating Metrics
Think through how you have defined, validated, and communicated success metrics for data science products, especially in ambiguous situations.
Technical Documentation Samples
Gather examples or outlines of technical design and architecture documentation you have produced for data science or AI products.
Cross-Functional Collaboration
Recall specific instances where you worked with product owners, engineering, or stakeholders to shape and deliver data products.
Questions to ask
, 6 itemsWhat are the most important business problems the Data Science & Analytics team is currently focused on?
Why ask this? To understand the team's priorities and how your work would drive impact.
How is success measured for data science products in this organization?
Why ask this? To clarify expectations and evaluation criteria for your work.
What is the typical workflow for collaborating with engineering and product teams on model deployment?
Why ask this? To learn about cross-functional processes and integration points.
How does the team handle ambiguous or incomplete data when developing models?
Why ask this? To gauge the organization's approach to real-world data challenges.
What tools and platforms are most commonly used for data transformation and modeling?
Why ask this? To confirm alignment with your technical skills and identify any learning needs.
How are priorities managed when multiple projects or stakeholders are involved?
Why ask this? To understand expectations for project management and communication.
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