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
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💰 - Day rate
500
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🗓️ - Date discovered
August 27, 2025
🕒 - Project duration
Unknown
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🏝️ - Location type
Remote
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📄 - Contract type
Outside IR35
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🔒 - Security clearance
Unknown
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📍 - Location detailed
United Kingdom
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🧠 - 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.