26 to 48 of 48 Time Series Forecasting Jobs in England

a financial institution, hedge fund, or tech firm Advanced degree (PhD or Master's) in Computer Science, Mathematics, Physics, Engineering, or related discipline Strong expertise in modern ML techniques: time-series forecasting, deep learning, ensemble methods, NLP, or RL Expert-level programming skills in Python and strong understanding of software engineering best practices Experience deploying ML models … to production in real-time or high-frequency environments Deep understanding of financial markets and quantitative modeling Preferred: Experience in front-office roles or collaboration with trading desks Familiarity with financial instruments across asset classes (equities, FX, fixed income, derivatives) Experience with distributed computing frameworks (e.g., Spark, Dask) and cloud-native ML pipelines Exposure to LLMs, graph learning, or More ❯

a financial institution, hedge fund, or tech firm Advanced degree (PhD or Master's) in Computer Science, Mathematics, Physics, Engineering, or related discipline Strong expertise in modern ML techniques: time-series forecasting, deep learning, ensemble methods, NLP, or RL Expert-level programming skills in Python and strong understanding of software engineering best practices Experience deploying ML models … to production in real-time or high-frequency environments Deep understanding of financial markets and quantitative modeling Preferred: Experience in front-office roles or collaboration with trading desks Familiarity with financial instruments across asset classes (equities, FX, fixed income, derivatives) Experience with distributed computing frameworks (e.g., Spark, Dask) and cloud-native ML pipelines Exposure to LLMs, graph learning, or 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 ❯

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 ❯

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 ❯

learning, and probability theory. Ideally this would be within sports or gaming/betting industries. Understanding of techniques such as Monte Carlo simulation, Bayesian modelling, GLMs, mixed effects models, time series forecasting etc Strong programming ability, preferably in Python SQL and relational databases The company offer some great benefits including a bonus, subsidised office meals, gym membership More ❯

learning, and probability theory. Ideally this would be within sports or gaming/betting industries. Understanding of techniques such as Monte Carlo simulation, Bayesian modelling, GLMs, mixed effects models, time series forecasting etc Strong programming ability, preferably in Python SQL and relational databases The company offer some great benefits including a bonus, subsidised office meals, gym membership More ❯

Performance & Scalability: Ensure data infrastructure is optimised for performance and can scale with growing data demands. Machine Learning Model Development: Lead the development of machine learning models, particularly for time-series forecasting (e.g., predicting on-site energy production). Data Preparation: Manage the transformation and preparation of datasets for model training and evaluation. Experimentation: Design and execute More ❯

Performance & Scalability: Ensure data infrastructure is optimised for performance and can scale with growing data demands. Machine Learning Model Development: Lead the development of machine learning models, particularly for time-series forecasting (e.g., predicting on-site energy production). Data Preparation: Manage the transformation and preparation of datasets for model training and evaluation. Experimentation: Design and execute More ❯

Performance & Scalability: Ensure data infrastructure is optimised for performance and can scale with growing data demands. Machine Learning Model Development: Lead the development of machine learning models, particularly for time-series forecasting (e.g., predicting on-site energy production). Data Preparation: Manage the transformation and preparation of datasets for model training and evaluation. Experimentation: Design and execute More ❯

Performance & Scalability: Ensure data infrastructure is optimised for performance and can scale with growing data demands. Machine Learning Model Development: Lead the development of machine learning models, particularly for time-series forecasting (e.g., predicting on-site energy production). Data Preparation: Manage the transformation and preparation of datasets for model training and evaluation. Experimentation: Design and execute More ❯

Performance & Scalability: Ensure data infrastructure is optimised for performance and can scale with growing data demands. Machine Learning Model Development: Lead the development of machine learning models, particularly for time-series forecasting (e.g., predicting on-site energy production). Data Preparation: Manage the transformation and preparation of datasets for model training and evaluation. Experimentation: Design and execute 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 ❯