1 to 25 of 26 Permanent NumPy Jobs in the UK

data processing: Kubernetes, Spark â Experience with Java 2D graphics and 3D OpenGL programming. â Experience with scientific computing libraries and frameworks: o Python: NumPy, SciPy, Pandas, TensorFlow (for ML/AI) o C Java: CUDA (for GPU acceleration) o Angular or React o Microservice: Quarkus, Spring Boot

people to the business this year, they're looking for Quantitative Developers with proficiency in Python, and experience with the Python data libraries (Pandas, NumPy) (preferably) from the Trading world. They also are on the look out for candidates who: Have deep familiarity with Python data ecosystem Understanding of Jupyter

people to the business this year, they're looking for Quantitative Developers with proficiency in Python, and experience with the Python data libraries (Pandas, NumPy) (preferably) from the Trading world. They also are on the look out for candidates who: Have deep familiarity with Python data ecosystem Understanding of Jupyter

unsupervised learning, natural language processing, Bayesian statistics, time-series forecasting, collaborative filtering etc) ? Proficiency in Python with familiarity to ML libraries e.g. pandas, numpy, scipy, scikit-learn, tensorflow, pytorch) ? Familiarity with cloud platforms (GCP, AWS, Azure) and tools like Dataiku, Databricks. ? Experience with ML Ops, including model deployment, monitoring

Derivatives trading desk using Python Implement automated testing with PyTest and ensure code quality through peer reviews Process, analyse, and manipulate data using NumPy and Pandas Manage CI/CD pipelines to ensure stable production environments Utilise Git-based workflows for collaborative development Work closely with team members and stakeholders

pipelines using tools like Azure DevOps, GitHub Actions, or GitLab CI, targeting Azure environments. Experience with popular Python libraries and frameworks such as Pandas, NumPy, and PySpark. Benefits Full-time, permanent contract. Competitive salary up to £60,000 per annum, depending on experience. Hybrid working model based in Aberdeen

preferred). Familiarity with MQTT, ZMQ, WebRTC, MIDI, or similar communication protocols. Experience building backend components, APIs, or control-plane systems. Desirable: FFMPEG, PyQt, NumPy, SQLAlchemy experience. Understanding of secure communications (SSL/TLS, JWT). Passion for scalable, highly available architecture (real-time or mission-critical systems).

PostgreSQL/PostGIS for spatial data management Familiarity with machine learning frameworks (e.g., TensorFlow, PyTorch, Scikit-learn) and data science tools (e.g., Jupyter, Pandas, NumPy) Ability to design, train, and evaluate supervised and unsupervised learning algorithms Experience working with large datasets, including data preprocessing Excellent communication skills, both written

language processing, computer vision, or predictive analytics Familiarity with machine learning frameworks (e.g., TensorFlow, PyTorch, Scikit-learn) and data science tools (e.g., Jupyter, Pandas, NumPy) Ability to design, train, and evaluate supervised and unsupervised learning algorithms Strong teamwork and interpersonal skills, with a collaborative and agile mindset Proven ability

based systems. Build, train, fine-tune, and evaluate AIML models for automation use cases. Develop Python-based data pipelines and algorithms using NLTK, NumPy, Scikit-learn, Pandas. Collaborate with cross-functional teams in an Agile setup (sprint planning, refinement, retrospectives). Integrate solutions with test automation frameworks and CI/

Skills & Experience Required Senior-level experience in data science or a quantitative field Proficient programming skills (Python preferred); familiarity with core data science libraries (NumPy, Pandas, Scikit-Learn) Experience with deep learning tools (e.g. PyTorch, TensorFlow, or similar) Strong mathematical/statistical background Familiarity with a wide range of data

Skills & Experience Required Senior-level experience in data science or a quantitative field Proficient programming skills (Python preferred); familiarity with core data science libraries (NumPy, Pandas, Scikit-Learn) Experience with deep learning tools (e.g. PyTorch, TensorFlow, or similar) Strong mathematical/statistical background Familiarity with a wide range of data

/ML, LLMs (GPT, Claude) and Generative AI . Proven experience with Agentic AI tools, GitHub Copilot, MCPs . Advanced Python skills – NLTK, NumPy, Pandas, Scikit-learn. Good understanding of AI/ML algorithms (Regression, Classification, Decision Trees, KNN, K-Means). Experience developing AI-enhanced test automation solutions. Solid

analysis and margin optimisation . Tech Environment Core Skills: SQL (essential), Power BI (advantageous) Platform: Microsoft Fabric (mid-implementation) Languages: Python (pandas/numpy - desirable, not essential) Other: Salesforce & Google Analytics exposure beneficial About You Proven experience as an Insight Analyst , ideally in wholesale, distribution, or another product-heavy industry.

looking for a talented ML Ops Engineer to join our growing teams. Key Responsibilities Strong proficiency in Python and ML libraries such as: pandas, NumPy, scikit-learn XGBoost, LightGBM, CatBoost TensorFlow, Keras, PyTorch Experience with model deployment and serving tools: ONNX, TensorRT, TensorFlow Serving, TorchServe Familiarity with ML lifecycle tools

Strong proficiency in Python programming including object-oriented design, asynchronous programming, error handling, and writing clean, maintainable code. Experience with key libraries including Pandas, NumPy for data manipulation, requests and APIs for integrations, asyncio for concurrent processing, and building robust automation scripts with proper logging, testing (pytest), and documentation.

Strong proficiency in Python programming including object-oriented design, asynchronous programming, error handling, and writing clean, maintainable code. Experience with key libraries including Pandas, NumPy for data manipulation, requests and APIs for integrations, asyncio for concurrent processing, and building robust automation scripts with proper logging, testing (pytest), and documentation.

time-series forecasting, survival analysis). Solid object-oriented programming skills in Python and hands-on expertise with the core data science stack (Pandas, Numpy, Scikit-learn, XGBoost/LightGBM). A proven ability to break down vague, complex problems into concrete, solvable steps. Excellent communication and data storytelling skills.

Build and deploy predictive ML models Mentor junior team members What you'll bring 4+ years' experience in data science Strong Python skills (pandas, numpy, PyTorch etc.) Experience with traditional ML techniques and statistical analysis Solid consulting skills - communication, problem-solving, stakeholder engagement Eligible for SC Security Check clearance Benefits

electronics, physics or computer science and have significant skills and experience in the following areas: - Expert Python proficiency for data analysis and prototyping (NumPy, SciPy, PyTorch). - Demonstrable applied experience of digital signal processing (FFT, modulation, noise modelling, resampling). - Experience with SDR frameworks (GNU Radio, SDRangel, SoapySDR). - Understanding

electronics, physics or computer science and have significant skills and experience in the following areas: - Expert Python proficiency for data analysis and prototyping (NumPy, SciPy, PyTorch). - Demonstrable applied experience of digital signal processing (FFT, modulation, noise modelling, resampling). - Experience with SDR frameworks (GNU Radio, SDRangel, SoapySDR). - Understanding

Engineer - AI for Advanced Materials - Oxford/Remote (UK) (Tech stack: Python, PyTorch, TensorFlow, Scikit-learn, MLflow, Airflow, Docker, Kubernetes, AWS, Azure, GCP, Pandas, NumPy, SciPy, CI/CD, MLOps, Data Visualization, Bayesian Modelling, Probabilistic Programming, Terraform) We're looking for a Machine Learning Engineer to join a rapidly scaling … some or all of the following (full training provided to fill any gaps): Python, PyTorch, TensorFlow, Scikit-learn, MLflow, Airflow, Docker, Kubernetes, Pandas, NumPy, SciPy, CI/CD, Data Visualization, Bayesian Modelling, Probabilistic Programming, Terraform, Azure, AWS, GCP, Git, and Agile methodologies. Join a team that's fusing AI, science

Language Models(GPT, Claude), Generative AI, Retrieval Augmented Generation. Agentic AI, CoPilot, MCPs. AIML Algorithms(Regression, Classification, Decision Trees, KNN, K-Means) Python (NLTK, NumPy, Scikit-learn, Pandas) Candidates will be expected to work on developing & implementing AIML Solutions for Test Automation in the Securities Processing space. This will entail … Front End experience in React to build front end for the AIML solutions is a plus. Hands- on experience with Python libraries like(NLTK, NumPy, Scikit-learn, Pandas). Knowledge of AIML algorithms (Regression, Classification, Decision Trees, KNN, K-Means) is preferred. Experience with building, training & finetuning AIML models

Support senior team members in identifying and addressing data science opportunities. Required Skills & Experience Strong proficiency in Python and ML libraries such as: pandas, NumPy, scikit-learn XGBoost, LightGBM, CatBoost TensorFlow, Keras, PyTorch Experience with model deployment and serving tools: ONNX, TensorRT, TensorFlow Serving, TorchServe Familiarity with ML lifecycle tools … . Excellent problem-solving skills and ability to work independently or in a team. Strong proficiency in Python and ML libraries such as: pandas, NumPy, scikit-learn XGBoost, LightGBM, CatBoost TensorFlow, Keras, PyTorch Experience with model deployment and serving tools: ONNX, TensorRT, TensorFlow Serving, TorchServe Familiarity with ML lifecycle tools

inference accuracy Developing ML and statistical models for RF classification, anomaly detection, and emitter identification Prototyping real-time or batch-processing systems in Python (NumPy, SciPy, PyTorch) with potential integration via ZMQ, GNU Radio, or C++ backends Leading RF data collection, field experiments, and over-the-air testing using drones … wireless devices, and custom transmitters Requirements Strong Python proficiency for RF data analysis and prototyping (NumPy, SciPy, matplotlib, scikit-learn, PyTorch) Solid understanding of DSP fundamentals (FFT, filtering, modulation, correlation, noise modelling, resampling) Familiarity with SDR frameworks such as GNU Radio, SDRangel, osmoSDR, or SoapySDR Practical understanding of RF hardware