Data Scientist
- Mid to senior Data Scientist role within a global business
- Full ML and AI lifecycle across traditional ML, LLMs, RAG and agentic AI workflows
- Belfast based, hybrid (2 days at home per week)
- Salary reflecting experience, competitive package
- UK work authorisation required
About the Company
Our client is a well-established US business with a growing Belfast technology team focused on data science, machine learning and platform development. Operating in a greenfield environment, the Belfast team brings new technologies and insights to the US market, working on genuinely innovative solutions that improve customer experience, reduce risk exposure and drive operational efficiency. With a culture of creativity, continuous learning and technical excellence, this is a compelling environment for a Data Scientist who wants to make their mark.
The Role
A mid to senior Data Scientist position covering the full ML and AI lifecycle, from data preparation and experimentation through to production deployment, monitoring and continuous improvement. You will work across traditional machine learning techniques alongside generative AI, LLM-based applications and agentic workflows, applying AWS technologies and robust MLOps practices to deliver scalable, reliable solutions. Working closely with technical teams, business stakeholders and subject matter experts, this role offers real variety, genuine ownership and a clear opportunity to grow within a collaborative and innovative team.
Key Responsibilities
- Design, develop, test and optimise machine learning and AI solutions addressing key business challenges including claims analysis, fraud detection, risk assessment and customer insights
- Develop NLP, LLM, RAG and agentic AI solutions that process unstructured data, generate actionable insights and enable multi-step automated workflows
- Build and maintain data pipelines ensuring data quality and accessibility for modelling purposes
- Deploy machine learning models to production environments on AWS, ensuring reliability and performance
- Contribute to MLOps practices including CI/CD pipelines, model monitoring, data drift detection and model retraining
- Partner with product teams and stakeholders to translate business requirements into data science solutions
- Document workflows, model development processes and performance metrics for transparency and collaboration
- Stay current on advancements in AI, ML and generative AI, contributing to a culture of continuous learning and knowledge sharing
What You'll Need
Essential:
- Bachelor's degree (minimum 2:1) in Computer Science, Data Science, Applied Mathematics, Statistics or a related quantitative field
- 4 or more years of experience in data science or machine learning roles
- Proven track record of building and deploying models in production environments
- Hands-on experience with AWS cloud services for machine learning and data engineering
- Proficiency in Python and data science libraries including scikit-learn, TensorFlow, Keras, Pandas and Seaborn
- Solid understanding of SQL and experience with NoSQL databases
- Hands-on experience building NLP, LLM, RAG and agentic AI solutions using frameworks such as LangChain, LangGraph, LlamaIndex and Hugging Face Transformers
- Familiarity with MLOps practices including Git, CI/CD and model lifecycle management
- Experience with containerisation tools such as Docker and orchestration tools such as Kubernetes
- Strong communication skills with the ability to present data science concepts to non-technical audiences
Desirable:
- Familiarity with modern data platforms including data lakes, lakehouses and workflow orchestration tools such as Dagster and dbt
- Knowledge of the property and casualty insurance industry including claims, underwriting, fraud detection or risk assessment
- Relevant AWS certifications such as AWS Certified AI Practitioner or AWS Certified Machine Learning Engineer
- MSc in Data Science or a related field
Why Apply?
- Competitive salary reflecting experience, with discretionary bonus potential
- 25 days annual leave plus 10 statutory and public holidays, plus birthday leave
- Pension with 9% total contribution and salary sacrifice option
- Health insurance, life assurance and income protection
- Employee Assistance Programme and a range of lifestyle benefits
- Hybrid working with 2 days at home per week
- Greenfield environment with genuine ownership and influence over technical direction
- Collaborative, innovation-focused team with a strong culture of continuous learning
Interested?
For a confidential conversation about this opportunity, connect with Justin Donaldson on LinkedIn or submit your CV via the link below.
Skills:
Python SQL NoSQL LLM RAG NLP MlOps
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