

Data Scientist
⭐ - Featured Role | Apply direct with Data Freelance Hub
This role is a Data Scientist position with a contract length of "unknown" and a pay rate of up to "£500 P/D". It requires proficiency in Python, experience in machine learning model validation, and knowledge of responsible AI principles. Remote work is available.
🌎 - Country
United Kingdom
💱 - Currency
£ GBP
-
💰 - Day rate
500
-
🗓️ - Date discovered
August 27, 2025
🕒 - Project duration
Unknown
-
🏝️ - Location type
Remote
-
📄 - Contract type
Outside IR35
-
🔒 - Security clearance
Unknown
-
📍 - Location detailed
United Kingdom
-
🧠 - Skills detailed
#Data Science #Libraries #Python #"ETL (Extract #Transform #Load)" #ML (Machine Learning) #Data Quality #Datasets #Data Engineering #Data Pipeline #NumPy #Pandas #API (Application Programming Interface) #Data Exploration #Compliance #AI (Artificial Intelligence) #DevOps
Role description
OUTSIDE IR35 + REMOTE – UP TOO £500 P/D
Inspirec has partnered with a dynamic and innovative leader in the technology industry, who are seeking a highly motivated AI/ML DevOps Engineer to join their team on a contract basis.
We are looking for a Data Scientist to develop and maintain high-quality data pipelines and analytics to support the training, evaluation, and validation of AI solutions. This role ensures that AI systems are built on trustworthy, well-understood data, and meet standards for accuracy, fairness, and robustness.
Key Responsibilities
• Prepare, clean, and transform large datasets for machine learning training and evaluation.
• Design and execute experiments to assess AI model performance across metrics such as accuracy, precision, recall, bias, and robustness.
• Collaborate with product and AI engineering teams to support data exploration and discovery.
• Ensure data quality, lineage, and adherence to governance and compliance standards.
• Analyse system usage and behaviour data to uncover insights and guide product and model development.
• Contribute to ethical AI assurance by identifying and documenting model limitations and risks.
Essential Skills & Experience
• Proficient in Python with a strong command of data science libraries such as pandas, NumPy, scikit-learn, and similar tools.
• Experience designing experiments and validating machine learning models with appropriate statistical rigor.
• Deep understanding of machine learning performance metrics and statistical evaluation techniques.
• Knowledge of responsible AI principles, including bias detection, fairness, and model transparency.
• Familiarity with modern data engineering practices, including ETL workflows, data pipelines, and API integration.
OUTSIDE IR35 + REMOTE – UP TOO £500 P/D
Inspirec has partnered with a dynamic and innovative leader in the technology industry, who are seeking a highly motivated AI/ML DevOps Engineer to join their team on a contract basis.
We are looking for a Data Scientist to develop and maintain high-quality data pipelines and analytics to support the training, evaluation, and validation of AI solutions. This role ensures that AI systems are built on trustworthy, well-understood data, and meet standards for accuracy, fairness, and robustness.
Key Responsibilities
• Prepare, clean, and transform large datasets for machine learning training and evaluation.
• Design and execute experiments to assess AI model performance across metrics such as accuracy, precision, recall, bias, and robustness.
• Collaborate with product and AI engineering teams to support data exploration and discovery.
• Ensure data quality, lineage, and adherence to governance and compliance standards.
• Analyse system usage and behaviour data to uncover insights and guide product and model development.
• Contribute to ethical AI assurance by identifying and documenting model limitations and risks.
Essential Skills & Experience
• Proficient in Python with a strong command of data science libraries such as pandas, NumPy, scikit-learn, and similar tools.
• Experience designing experiments and validating machine learning models with appropriate statistical rigor.
• Deep understanding of machine learning performance metrics and statistical evaluation techniques.
• Knowledge of responsible AI principles, including bias detection, fairness, and model transparency.
• Familiarity with modern data engineering practices, including ETL workflows, data pipelines, and API integration.