innovation and value across a data-rich business environment. Key Responsibilities: Design, build, and deploy data science solutions to support business initiatives, with a focus on machine learning and timeseries forecasting. Collaborate with cross-functional teams to integrate ML models into production systems using modern cloud platforms. Communicate insights and model outputs to stakeholders in a clear … Proficient in SQL for data extraction and transformation. Experience with Google Cloud Platform (GCP) and Vertex AI for developing and deploying ML services is highly desirable. Solid understanding of timeseries analysis and forecasting techniques. Strong foundation in computer science principles - data structures, algorithms, software architecture, and data modelling. Deep understanding of machine learning algorithms including but More ❯
athletic performance, fan engagement, and predictive analytics in the sports industry. You'll be part of a highly skilled R&D team building next-generation AI solutions for real-time insights, performance optimisation, and immersive sports analytics. THE ROLE Design, develop, and deploy AI/ML models focused on sports analytics, predictive modelling, and computer vision. Collaborate with data … scientists, software engineers, and sports analysts to translate real-world data into actionable insights. Optimise AI systems for real-time environments, integrating with live data feeds and cloud infrastructure. Research and prototype cutting-edge AI techniques (e.g., deep learning, reinforcement learning, generative models). Support continuous model improvement and scalable MLOps deployment pipelines. TECH STACK/REQUIREMENTS Core Skills … Python, TensorFlow/PyTorch, scikit-learn, OpenCV, NumPy, Pandas Experience With: Model training, tuning, and deployment in production environments Preferred: Sports data analytics, time-seriesforecasting, or computer vision experience Infrastructure: AWS/GCP/Azure, Docker, Kubernetes, MLflow, CI/CD pipelines Bonus: Experience with wearable/sensor data, player tracking, or sports video analytics TO More ❯
London, South East, England, United Kingdom Hybrid / WFH Options
Sanderson
. Data transformation & manipulation : experience with Airflow, DBT and Kubeflow Cloud: Experience with GCP and Vertex AI (developing ML services). Expertise: Solid understanding of computer science fundamentals and time-series forecasting. Machine Learning: Strong grasp of ML and deep learning algorithms (e.g. Logistic Regression, Random Forest, XGBoost, BERT, LSTM, NLP, Transfer Learning). Reasonable Adjustments: Respect and More ❯
Machine Learning Engineer Solid knowledge of SQLandPython's ecosystem for data analysis (Jupyter, Pandas, Scikit Learn, Matplotlib). GCP, VertexAI experience is desirable (developing GCP machine learning services) Timeseries forecasting Solid understanding of computer science fundamentals, including data structures, algorithms, data modelling and software architecture Strong knowledge of Machine Learning algorithms (e.g. Logistic Regression, Random Forest, XGBoost, etc.) as … well as state-of-the-art research area (e.g. NLP, Transfer Learning etc.) and modern Deep Learning algorithms (e.g. BERT, LSTM, etc.) Machine Learning Engineer, timeseries, forecasting, VertexAI, GCP Reasonable Adjustments: Respect and equality are core values to us. We are proud of the diverse and inclusive community we have built, and we welcome applications from people of all More ❯