6 of 6 Remote Time Series Forecasting Jobs in Central London

foundation Python Data Science Stack : Advanced proficiency in Python , including Pandas, NumPy and scikit-learn. Statistical & Machine Learning Modelling : Experience with a variety of ML techniques ( regression, classification, clustering, time-series forecasting ) Experience with deep learning frameworks such as Keras or PyTorch Model Deployment : Proven ability to productionise models, including building and deploying APIs Strong visualization and More ❯

foundation Python Data Science Stack : Advanced proficiency in Python , including Pandas, NumPy and scikit-learn. Statistical & Machine Learning Modelling : Experience with a variety of ML techniques ( regression, classification, clustering, time-series forecasting ) Experience with deep learning frameworks such as Keras or PyTorch Model Deployment : Proven ability to productionise models, including building and deploying APIs Strong visualization and More ❯

Jupyter Notebooks . Statistical & ML Modelling: Strong foundation in statistical analysis and proven experience applying a range of machine learning techniques to solve business problems (e.g., regression, classification, clustering, time-series forecasting). Practical experience with Keras or PyTorch is required. Full-Stack Deployment: Demonstrable experience taking models to production, including building and deploying APIs with FastAPI More ❯

Azure/AWS Key Responsibilities: Partner with cross-functional teams to translate business pain points into scalable data science solutions. Architect and deploy ML models (with a focus on time-series forecasting) to optimise field operations. Own the full analytics lifecyclefrom data wrangling and pipeline design to model deployment and stakeholder storytelling. Mentor junior data scientists and More ❯

Azure/AWS Key Responsibilities: Partner with cross-functional teams to translate business pain points into scalable data science solutions. Architect and deploy ML models (with a focus on time-series forecasting) to optimise field operations. Own the full analytics lifecycle—from data wrangling and pipeline design to model deployment and stakeholder storytelling. Mentor junior data scientists More ❯

Azure/AWS Key Responsibilities: Partner with cross-functional teams to translate business pain points into scalable data science solutions. Architect and deploy ML models (with a focus on time-series forecasting) to optimise field operations. Own the full analytics lifecycle—from data wrangling and pipeline design to model deployment and stakeholder storytelling. Mentor junior data scientists More ❯