Data Scientist – Developer
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At a glance
- Employment type
- Contract
- Workplace
- Hybrid · 2 days in office
- Category
- Data engineering
Technologies & skills
What you'll be doing
Contract Data Scientist/Developer role in Dublin, joining a growing Data Innovation hub to build and deliver AI products and services for large enterprises. Work on NLP and LLM products, collaborate on new ML/AI applications, and leverage a Python data stack in a hybrid work environment.
- Join the existing data science team and work on new and existing algorithms
- Develop NLP and LLM products following a DevOps approach
- Collaborate on strategic plans to help define new ML AI applications
Key requirements
Must-have
- Strong experience with Python data stack (NumPy, Pandas, etc.)
- Experience across the entire data science lifecycle
- Experience with ML and analysis techniques for real world use cases
- Experience in data modelling
- Experience in Azure DevOps approach to productionization
- Experience working with large datasets
Role signals
- Technical focus
- data science, AI product development, NLP, LLMs
- Hands-on vs management
- Hands-on
Similar jobs
Full job description
Data Scientist / Developer – Contractopportunity with an expanding Data Innovation hub in Dublin City centre. Our Client creates a suite of AI products for large enterprises across a diverse portfolio of sectors. They access a wide source of business data from across the globe, which offers a unique opportunity to design, lab-test and trial scalable business solutions in a reliable and secure environment.
This is a unique opportunity to join a growing business and help build and successfully deliver cutting edge AI products and Data and AI services.
Initially a 6-month day rate contract. Its hybrid working from Dublin City Centre office (2-3 days / week.
Responsibilities
-
Join the existing DS team and work on new and existing algorithms
-
Develop NLP and LLM products following a DevOps approach (Agile, Git, Auto Testing and CI/CD etc)
-
Collaborate on strategic plans to help define new ML AI applications.
Requirements
-
Strong experience of python data stack such as (numpy, scipy, sklearn / scikit-learn, pandas etc)
-
Experience across the entire data science lifecycle (requirements gathering, design, implementation, testing and deployment)
-
Experience of ML and analysis techniques for real world use cases
-
Experience in data modelling.
-
Experience in Azure DevOps approach to productionization
-
Experience working with large datasets
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Interview prep pack
Grounded in this listing. Use it to prepare examples before you apply.
Your interview focus
Based on this listing, the role is a contract Data Scientist/Developer position focused on building and delivering AI products, particularly NLP and LLM solutions, using Python and Azure DevOps in a hybrid Dublin-based environment.
- Hands-on AI product development·High
- DevOps and productionization·High
- NLP/LLM expertise·Medium
Only have 30 minutes?
Follow a focused preparation plan based on this job.
Start 30-minute prep
Your 30-minute plan
Review Python Data Stack Projects
0–7 minSelect 1-2 projects where you used NumPy, Pandas, or similar libraries to process large datasets and prepare concise summaries.
Refresh Azure DevOps and CI/CD Knowledge
7–13 minRevisit your experience with Azure DevOps, focusing on CI/CD pipelines and automation in data science workflows.
Prepare NLP/LLM Case Studies
13–20 minIdentify examples where you developed or deployed NLP or LLM solutions, emphasizing business impact and technical challenges.
Map Out Data Science Lifecycle Example
20–25 minOutline a project where you managed the full data science lifecycle, noting your contributions at each stage.
Draft Role-Specific Questions
25–30 minWrite down 2-3 thoughtful questions about the team, projects, and workflows to ask during the interview.
Talking points
, 5 itemsPython Data Stack (NumPy, Pandas, etc.)
Demonstrate your ability to manipulate, analyze, and model large datasets efficiently using core Python libraries.
End-to-End Data Science Lifecycle
Showcase experience in requirements gathering, design, implementation, testing, and deployment of data science solutions.
NLP and LLM Product Development
Highlight your hands-on work with natural language processing and large language models, especially in production environments.
Azure DevOps and CI/CD
Illustrate your familiarity with deploying and maintaining data science solutions using DevOps practices and Azure tools.
Collaboration and Strategic Planning
Provide examples of working within teams to define and deliver new machine learning and AI applications.
What to research
, 4 itemsPython Data Stack Proficiency
Review and prepare examples of using NumPy, Pandas, and related libraries for large-scale data analysis.
Azure DevOps and CI/CD
Refresh your knowledge of setting up and managing CI/CD pipelines for data science projects in Azure.
NLP and LLM Implementation
Prepare to discuss hands-on experience with NLP and large language models, including deployment and real-world use cases.
End-to-End Data Science Lifecycle
Be ready to walk through a project from requirements gathering to deployment, emphasizing your role at each stage.
Questions to ask
, 6 itemsWhat types of AI products and NLP/LLM solutions are currently being developed by the team?
Why ask this? Clarifies the technical focus and allows you to align your experience with their projects.
How is the data science team structured, and how do team members typically collaborate?
Why ask this? Helps you understand team dynamics and collaboration expectations.
What are the main challenges faced when deploying AI solutions in production at your organization?
Why ask this? Gives insight into technical and operational hurdles you may encounter.
How is success measured for data science projects within the company?
Why ask this? Clarifies performance expectations and key metrics.
What opportunities exist for contributing to the strategic direction of new ML/AI applications?
Why ask this? Assesses your potential for involvement in planning and innovation.
What is the typical workflow for moving a model from development to production using Azure DevOps?
Why ask this? Helps you understand the deployment process and your role in it.
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