14 of 14 Permanent Time Series Forecasting Jobs in the South East

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 ❯

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 ❯

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 ❯

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 ❯

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 ❯

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 ❯

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 ❯