13 of 13 Permanent Time Series Forecasting Jobs in Central London

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 ❯

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 ❯

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 ❯

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 ❯

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 ❯