1 to 25 of 28 Time Series Forecasting Jobs in England

working closely with AI and research functions to design, implement, and maintain scalable ML models for forecasting. This role offers the opportunity to work at the cutting edge of time-series forecasting within a fast-paced and intellectually rigorous environment. Key Responsibilities Building machine learning models, with a focus on time-series forecasting. Collaborating with … Strong experience with cloud platforms (AWS, GCP, or Azure), Docker, and Kubernetes. Solid coding practices, including Git, automated testing, and CI/CD. Proficiency with Linux environments. Knowledge of time-series analysis is a strong plus. Please note, this role cannot offer VISA sponsorship. More ❯

working closely with AI and research functions to design, implement, and maintain scalable ML models for forecasting. This role offers the opportunity to work at the cutting edge of time-series forecasting within a fast-paced and intellectually rigorous environment. Key Responsibilities Building machine learning models, with a focus on time-series forecasting. Collaborating with … Strong experience with cloud platforms (AWS, GCP, or Azure), Docker, and Kubernetes. Solid coding practices, including Git, automated testing, and CI/CD. Proficiency with Linux environments. Knowledge of time-series analysis is a strong plus. Please note, this role cannot offer VISA sponsorship. More ❯

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/Qualifications Proven … 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 ❯

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 ❯

ML pipelines for time-series forecasting. Central London office based £90-120k+ highly negotiable Discretionary bonus 50-100% of salary An opportunity for an elite Machine Learning Engineer to join a very small, top-flight AI team (2-6 people within a larger international group) with arguably the leading macro hedge fund. The bonus potential is unparalleled. … ML pipelines for forecasting. You will prize taking ownership of the work, devising your own plans, impacting directly on the forecast. Responsibilities: Build scalable machine learning pipelines for predominantly time-series forecasting Collaborate with data scientists and researchers to productionise models Manage cloud-based and on-prem setup Requirements: 25 years experience in ML engineering/MLOps … Databricks, etc.) Experience with cloud platforms (AWS, GCP/Vertex, Azure), Docker, and Kubernetes Solid coding practices (Git, automated testing, CI/CD). Proficiency with Linux Familiarity with time-series analysis Experience in financial services Above all were seeking incredibly smart, driven people. More ❯

the globe. This role offers a chance to build models, generate insights, and work directly with real-world product and customer data across multiple data types - text, images, and time-series (tabular) data. The Role You’ll join a small, collaborative data team in a Full stack Data Science role (ETL process through to building predictive models) working … on live analytics, forecasting, and AI/ML projects. Key responsibilities include: Enriching and preparing datasets to uncover actionable insights Building and evaluating time-series forecasts Prototyping AI/ML models (NLP and computer vision exposure) Translating findings into clear visual reports and concise business narratives Contributing to simple, reliable data pipelines and automated quality checks About … You Minimum 1 year of commercial Data Science experience Strong hands-on Python (pandas, NumPy, scikit-learn) and SQL Sound understanding of statistics and forecasting Confident communicator - able to explain technical work clearly to non-technical audiences Why Apply Opportunity to build from scratch within a growing data function Exposure to cutting-edge work in NLP, time-series More ❯

tackle physical climate risks. You will be tasked with defining the ML strategy, assembling a high-impact team, and driving real-world innovations in generative AI, geospatial modelling, and time-series forecasting. Responsibilities Develop and refine the company's AI & ML strategy in alignment with product goals and customer needs. Lead, mentor, and expand a talented team of … for assessing geospatial risks. Building predictive models to forecast the impacts of floods, cyclones, and heat stress. Utilizing satellite imagery and remote sensing technology for climate hazard detection. Enhancing time-series forecasting to inform risk planning at the asset level. Integrating simulation data with ML for combined physical and statistical modelling. What We're Looking For Demonstrated More ❯

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 time series 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 time series 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 ❯

it cost Amazon to deliver? The WW Amazon Logistics, Business Analytics team manages the delivery of tens of millions of products every week to Amazon's customers, achieving on-time delivery in a cost-effective manner. We are seeking an enthusiastic, customer-obsessed Principal Applied Scientist with strong analytical skills to join our team. This role is crucial in … the core challenges in our world class operations space! Key job responsibilities Advanced Modeling and Algorithm Development: Design and implement sophisticated machine learning models for logistics optimization Develop complex time series forecasting algorithms for demand prediction and resource allocation AI and Machine Learning Integration: Architect and deploy AI powered systems to enhance decision making in logistics operations 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-series forecasting, 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 ❯

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 ❯

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 ❯