15 of 15 Remote Time Series Forecasting Jobs in London

findings in a compelling and accessible way. Monitoring and fine-tuning model performance to ensure continuous accuracy and reliability. Using advanced machine learning techniques (including NLP, deep learning, and time-series forecasting) to solve complex business challenges. Contributing to the development of robust MLOps processes for deployment, monitoring, and retraining of models. About You You're a … familiarity across ML and data libraries (pandas, numpy, scipy, scikit-learn, tensorflow, pytorch). A broad understanding of machine learning and statistical methods, including NLP, deep learning, Bayesian inference, time-series forecasting, and recommendation systems. Hands-on experience with cloud platforms such as AWS, GCP, or Azure, and exposure to tools like Databricks or Dataiku. Practical knowledge 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 ❯

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 ❯

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 ❯

data teams to ensure scalable solutions Requirements Extensive experience in data science, including applied statistics and machine learning Familiarity with supervised and unsupervised learning, NLP, neural networks, Bayesian statistics, time-series forecasting, collaborative filtering, and deep learning Proficiency in Python and ML libraries (e.g. pandas, numpy, scipy, scikit-learn, tensorflow, pytorch) Experience with cloud platforms (GCP, AWS 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 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 ❯

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 ❯

. 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 ❯