10 of 10 Neural Network Jobs in Central London

engineering and building robust data pipelines for AI/ML workloads on GCP (e.g., BigQuery, Dataflow). • Solid understanding of core AI/ML concepts, including deep learning, neural networks, NLP, and machine learning algorithms. • Familiarity with MLOps principles and tools for deploying, managing, and monitoring ML models in production. • Excellent problem-solving skills, with a passion for More ❯

A degree in Artificial Intelligence, Data Science, Computer Science, Mathematics, or a related field. Experience or coursework in machine learning, deep learning, or AI techniques (e.g., supervised learning, neural networks, NLP). Proficiency in Python and libraries like Scikit-learn, TensorFlow, PyTorch, or Hugging Face Transformers. Familiarity with data preprocessing, model evaluation, and deploying models in real-world More ❯

pipelines (realtime or batch) & data quality using modern toolchain (e.g., Apache Spark, Kafka, Airflow, dbt). Strong foundational knowledge of machine learning and deep learning algorithms, including deep neural networks, supervised/unsupervised learning, predictive analysis, and forecasting. Expert-level proficiency in Python, with a demonstrated ability to develop and debug production-grade code. Desired Skills (Bonus Points More ❯

field; or Master’s degree with 2+ years of relevant industry experience; or Bachelor’s degree with 4+ years of relevant industry experience. Strong expertise in deep learning, neural networks, and generative models (GANs, diffusion models). Practical experience with modern machine learning frameworks (e.g., PyTorch, TensorFlow). Advanced programming skills in Python . Strong problem-solving, analytical More ❯

to tackle hard problems in quantitative finance: Build machine learning trading strategies across a range of asset classes Design predictive models with scientific rigor Explore new projects in neural networks and deep learning Oversee projects focused on finding trading product solutions Requirements for the Quantitative Researcher - Machine Learning position: PhD in a technical or quantitative discipline such as More ❯

equivalent experience in a relevant discipline e.g. engineering, mathematics, physics, statistics Experience of data science in finance, insurance or Ecommerce is an advantage but not required. Experience with neural networks and Tensor Flow, CatBoost, XGBoost, SKlearn, Pandas If you hold the experience and technical skills outlined above which would enable you to hit the ground running, please apply More ❯

Computer Science, Machine Learning, or a closely related field Strong foundation in machine learning and deep learning algorithms (e.g., transformers, GNNs, supervised/unsupervised learning, reinforcement learning, deep neural networks) Excellent Python programming skills with experience in developing and debugging production-level code Desired Skills (Bonus Points): Proven experience in recommender systems, behavioural AI, and/or reinforcement More ❯

expansion, the company is looking to onboard a Senior Data Scientist with strong commercial experience in building Recommender Systems. Experience in designing and deploying Graph Models or Graph Neural Networks would be a significant advantage. Responsibilities Work with senior colleagues and internal stakeholders to spot business opportunities to leverage data science techniques and add business value. Build relevant … but all backgrounds considered). Excellent Python skills. Strong Machine Learning & Statistical knowledge Commercial Recommendation Engine experience Software Development best practice approach (CI/CD experience, etc.) Graph Neural Networks/Knowledge Graph experience. If this role interests you and you would like to learn more, please apply here or contact us via niall.wharton@Xcede.com (feel free to More ❯

preparation, exploratory analysis, model training, validation, and deployment. Develop and fine-tune predictive algorithms such as: Classification: Logistic Regression, Decision Trees, Random Forests. Regression: Linear Models, Gradient Boosting, Neural Networks, K-Nearest Neighbors. Build models for customer behavior prediction and risk assessment in insurance, particularly in underwriting and claims. Apply natural language processing (NLP) for tasks like document More ❯

Previous experience within Personal Lines Pricing is advantageous Experience with some of the following predictive modelling techniques; Logistic Regression, GBMs, Elastic Net GLMs, GAMs, Decision Trees, Random Forests, Neural Nets and Clustering Experience in statistical and data science programming languages (e.g. R, Python, PySpark, SAS, SQL) A good quantitative degree (Mathematics, Statistics, Engineering, Physics, Computer Science, Actuarial Science More ❯