Data Scientist

6 months Remote Active SC security clearance and eligible candidates will be considered Inside IR35 - Umbrella only Role Description The role itself is assisting the Border Force Officers identify transport with suspicious cargo coming through customs. Using Computer Vision and Machine Learning modelling, Officers will be able to identify cargo for further inspection based up image matching, which will reduce the chance of items coming through borders. It is hoped that this will increase the speed of checks, reduce loss of revenue from taxing and increase throughput rate We are seeking a skilled Computer Vision Data Scientist to join our team and lead the integration of machine learning (ML) models into the ScanApp application. The ideal candidate will possess a strong background in Python, computer vision, and deep learning, as well as experience with large language models (LLMs). You will be responsible for refining existing models, setting up in-house infrastructure, and ensuring seamless integration with the ScanApp. Key Responsibilities • Collaborate with cross-functional teams to understand ScanApp requirements and translate them into robust ML models. • Develop, train, and optimise computer vision and deep learning models using Python and popular libraries (e.g., TensorFlow, PyTorch). • Integrate ML models into the ScanApp application, ensuring compatibility and scalability. • Utilize knowledge of LLMs to enhance model outputs and improve user experience for Border Force officers. • Establish and maintain efficient, reliable, and secure in-house ML infrastructure, either by adapting code from the trial phase or devising new solutions based on trial learnings. • Conduct thorough model testing, validation, and iteration to ensure accuracy, reliability, and performance. • Strong Stakeholder Management Skills All profiles will be reviewed against the required skills and experience. Due to the high number of applications we will only be able to respond to successful applicants in the first instance. We thank you for your interest and the time taken to apply!