4 of 4 Anomaly Detection Jobs in the North West

building intelligent agents that detect anomalies and glitches in gameplay, ensuring an exceptional player experience. Key Responsibilities Design and implement ML models for anomaly detection in video streams and gameplay data. Apply computer vision and video analysis techniques to identify irregularities and bugs. Explore advanced methods such … background in machine learning research; PhD preferred (or equivalent experience in game development). Expertise in Python, PyTorch, and TensorFlow. Experience with video-based anomaly detection, computer vision, and streaming analysis. Gaming industry experience is a plus. Ability to lead projects and guide technical direction ...

communicate findings in clear, non-technical terms. Modelling, experimentation, and impact Build and deploy predictive and prescriptive models (predictive maintenance, yield/defect forecasting, anomaly detection, capacity planning, SPC-aware models). Deployment, governance, and scale Operationalise models in the cloud with robust MLOps practices, including versioning … monitoring, drift detection, retraining, and documentation. Create model cards and explainability artifacts to support trust and adoption by operators and leadership. Develop scalable data pipelines and reusable analytics components; enable near real-time scoring and alerting for process deviations. Collaboration, enablement, and change management Partner with process engineers ...

. Libraries such as NetworkX , TensorFlow or PyTorch , NLTK, spaCy, Hugging Face, BERT , Pandas, NumPy, scikit learn . Graph based ML familiarity: link prediction, anomaly detection, traversal, community detection. NLP/NLU skills: entity/relation recognition, summarisation, topic modelling, classification, coreference resolution. Visualization and communication Graph ...

dashboards and visualisations, communicating insights in accessible, non-technical language Modelling & Deployment Develop and deploy predictive and prescriptive models (e.g. predictive maintenance, defect forecasting, anomaly detection, capacity planning) Operationalise models using cloud and MLOps best practices, including monitoring, documentation, retraining, and explainability Collaboration & Change Work closely with ...