26 to 50 of 58 Time Series Forecasting Jobs in the UK

ML with tangeable environmental impact. As a Machine Learning Engineer, you will: Build and deploy ML models that drive decision-making and automation in complex physical systems Develop advanced forecasting and modelling approaches for challenging datasets Productionise research-grade ML using modern MLOps tooling Influence architecture and best practices across a growing technical team Given the number of candidates … PyTorch - production experience with Docker/AWS Proven track record of taking models from concept to production with measurable impact - ROI/Gains/Drops/Reducations Experience with time-series forecasting/analysis Experience with forecasting and control/optimisation techniques Experience of MLOps is highly advantageous but not an absolute pre-requisite for the More ❯

ML with tangeable environmental impact. As a Machine Learning Engineer, you will: Build and deploy ML models that drive decision-making and automation in complex physical systems Develop advanced forecasting and modelling approaches for challenging datasets Productionise research-grade ML using modern MLOps tooling Influence architecture and best practices across a growing technical team Given the number of candidates … PyTorch - production experience with Docker/AWS Proven track record of taking models from concept to production with measurable impact - ROI/Gains/Drops/Reducations Experience with time-series forecasting/analysis Experience with forecasting and control/optimisation techniques Experience of MLOps is highly advantageous but not an absolute pre-requisite for the 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 ❯

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 ❯

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 ❯