1 to 25 of 50 Time Series Forecasting Jobs in the UK

Data Scientist – Energy Markets & Forecasting London (Hybrid, 2–3 days/week) | 💰 £Competitive + Equity Climate Tech | Tier-1 VC Backing | Real-Time Systems An elite climate tech startup is looking for a Data Scientist with deep experience in time-series forecasting to help build real-time models for trading and energy optimisation. This … is a rare opportunity to apply advanced analytics to one of the most urgent challenges of our time: decarbonising the grid. You’ll work on high-variance, noisy datasets, collaborating directly with traders and market experts to deploy models that drive real decisions. Why This Role? Solve complex forecasting problems in real-time energy markets Collaborate across … Who You Are We’re looking for someone with: A top-tier academic background in Maths, Physics, Engineering, or CS (Oxbridge, Imperial, top international equivalents) Hands-on expertise in time-series, forecasting, or signal processing Strong Python skills and experience writing clean, maintainable code A sharp, systems-level mindset with commercial awareness Bonus: knowledge of energy markets More ❯

Data Scientist – Energy Markets & Forecasting London (Hybrid, 2–3 days/week) | 💰 £Competitive + Equity Climate Tech | Tier-1 VC Backing | Real-Time Systems An elite climate tech startup is looking for a Data Scientist with deep experience in time-series forecasting to help build real-time models for trading and energy optimisation. This … is a rare opportunity to apply advanced analytics to one of the most urgent challenges of our time: decarbonising the grid. You’ll work on high-variance, noisy datasets, collaborating directly with traders and market experts to deploy models that drive real decisions. Why This Role? Solve complex forecasting problems in real-time energy markets Collaborate across … Who You Are We’re looking for someone with: A top-tier academic background in Maths, Physics, Engineering, or CS (Oxbridge, Imperial, top international equivalents) Hands-on expertise in time-series, forecasting, or signal processing Strong Python skills and experience writing clean, maintainable code A sharp, systems-level mindset with commercial awareness Bonus: knowledge of energy markets 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 ❯

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 ❯

Science vision and roadmap , aligned with business priorities. Provide technical oversight for AI initiatives across domains: Generative AI & LLMs (fine-tuning, RAG pipelines, multi-agent systems). Predictive Analytics & Time-Series Modeling . Computer Vision & Multimodal AI . Reinforcement Learning & Optimization . Knowledge Engineering & Semantic Search . Edge AI & Real-Time AI Deployments . Act as the … experience in AI/ML, including experience in a technical leadership or team lead role . Strong knowledge (architectural & practical) of: LLMs, RAG, and AI Agents . Predictive analytics & time-series forecasting . Computer vision, multimodal learning, and geospatial AI . Reinforcement learning and optimization techniques . MLOps practices & data pipelines . Ability to review code, design More ❯

leadership and people management skills, with at least 3 years of experience building and developing high-performing teams.5+ years of experience in data science, preferably with a focus on time series forecasting, FMCG, Food & Beverages, Retail or similar industries with a proven track record of delivering effective business solutions.Experience in application of ML concepts and methodologies (particularly … time series modeling, but also regression, classification, feature engineering and selection etc.)Proficiency in SAS, SQL, Python, and other programming languages to communicate effectively with technical teams. Excellent communication and presentation skills, ability to explain complex analytical topics to both technical and non-technical stakeholders. What we offer All kinds of benefits depending on the location.At Mondelēz International 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 ❯

Conduct data exploration, analysis, and preprocessing for large structured/unstructured datasets. Collaborate with cross-functional teams to integrate models into business systems. Apply predictive analytics, statistical modelling, and time series forecasting to drive decision-making. Maintain clear documentation for all AI/ML pipelines and workflows . Key Skills: Solid hands-on experience in AI/ 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 ❯

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 ❯

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 ❯

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 ❯