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Data Science Intern

Job in Belfast, County Antrim, BT1, Northern Ireland, UK
Listing for: Confidential
Apprenticeship/Internship position
Listed on 2026-11-02
Job specializations:
  • IT/Tech
    Data Scientist, Machine Learning/ ML Engineer, Data Analyst, Data Engineering
Salary/Wage Range or Industry Benchmark: 65000 - 90000 GBP Yearly GBP 65000.00 90000.00 YEAR
Job Description & How to Apply Below

We are hiring a skilled and motivated Data Scientist on behalf of one of our clients for a fully remote opportunity.

The ideal candidate will have strong analytical and problem-solving skills, hands-on experience with data science tools, and the ability to transform complex datasets into actionable business insights.

Job Title: Data Scientist

Location: United Kingdom

Work Mode: 100% Remote

Key Responsibilities
  • Collect, clean, preprocess, and analyse large datasets from multiple sources.
  • Perform exploratory data analysis (EDA) to identify trends, patterns, and business opportunities.
  • Develop, train, test, and optimise machine learning models for predictive analytics and forecasting.
  • Apply statistical techniques and hypothesis testing to support data-driven decision-making.
  • Build predictive models and implement algorithms to solve business problems.
  • Create dashboards, reports, and visualisations using Power BI, Tableau, Matplotlib, or Seaborn.
  • Write and optimise SQL queries to extract and manage data from databases.
  • Use Python libraries such as Pandas, Num Py, Scikit-learn, and Sci Py for data analysis and modelling.
  • Identify and resolve data quality issues to ensure accuracy and reliability.
  • Collaborate with data engineers, business analysts, developers, and other stakeholders.
  • Translate business requirements into analytical solutions and communicate findings effectively.
  • Monitor model performance and continuously improve machine learning solutions.
  • Automate data processing and reporting workflows to improve efficiency.
  • Perform feature engineering and feature selection to enhance model performance.
  • Document analytical processes, methodologies, and project outcomes.
  • Ensure data privacy, security, and compliance with applicable data protection requirements.
Required Skills & Qualifications
  • Strong programming skills in Python and SQL.
  • Good understanding of statistics, probability, and machine learning algorithms.
  • Hands-on experience with Pandas, Num Py, Scikit-learn, and data visualisation libraries.
  • Familiarity with Power BI or Tableau.
  • Excellent analytical, problem-solving, and communication skills.
  • A degree in Data Science, Computer Science, Statistics, Mathematics, or a related field is preferred.
Preferred Qualifications
  • Experience working with real-world datasets, predictive modelling, and advanced statistical analysis.
  • Familiarity with cloud platforms such as AWS, Microsoft Azure, or Google Cloud Platform (GCP).
  • Knowledge of deep learning frameworks such as Tensor Flow, PyTorch, or Keras.
  • Experience with natural language processing (NLP), computer vision, or generative AI applications.
  • Understanding of big data technologies such as Apache Spark, Hadoop, or Databricks.
  • Experience with ETL pipelines, data integration, and data preprocessing workflows.
  • Familiarity with database systems such as PostgreSQL, MySQL, MongoDB, or Snowflake.
  • Knowledge of model deployment using Flask, FastAPI, Docker, or cloud-based services.
  • Understanding of MLOps practices, including model versioning, monitoring, and lifecycle management.
  • Experience using Git or other version control systems for collaborative development.
  • Familiarity with A/B testing, experimental design, and business performance metrics.
  • Ability to work with time-series data, forecasting techniques, and anomaly detection.
  • Experience developing scalable and production-ready machine learning solutions.
  • Understanding of responsible AI practices, model explainability, and bias detection.
  • Experience working in Agile environments and collaborating with cross-functional teams.
  • A portfolio of data science projects, research work, or contributions to open-source projects is an advantage.
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