21 of 21 Jupyter Jobs in England

that serve real users at scale without incurring technical debt. Proficiency in statistical methods and experience following CRISP-DM data science lifecycle. Expertise taking projects from ideation or experimental Jupyter notebooks to full production deployment. Strong programming skills in Python, with familiarity in ML libraries/frameworks such as TensorFlow, PyTorch, and Scikit-learn. Experience with MLOps practices including model More ❯

to Have (But Not Required): Experience from a university project, hackathon, or internship involving AI or machine learning Understanding of neural networks, NLP, or computer vision concepts Experience using Jupyter notebooks, Git, or cloud platforms (AWS, GCP, Azure) Knowledge of SQL or data wrangling techniques What You’ll Gain Real-World Experience : Work on live AI projects with real business More ❯

to Have (But Not Required): Experience from a university project, hackathon, or internship involving AI or machine learning Understanding of neural networks, NLP, or computer vision concepts Experience using Jupyter notebooks, Git, or cloud platforms (AWS, GCP, Azure) Knowledge of SQL or data wrangling techniques What You’ll Gain Real-World Experience : Work on live AI projects with real business More ❯

to Have (But Not Required): Experience from a university project, hackathon, or internship involving AI or machine learning Understanding of neural networks, NLP, or computer vision concepts Experience using Jupyter notebooks, Git, or cloud platforms (AWS, GCP, Azure) Knowledge of SQL or data wrangling techniques What You’ll Gain Real-World Experience : Work on live AI projects with real business More ❯

to Have (But Not Required): Experience from a university project, hackathon, or internship involving AI or machine learning Understanding of neural networks, NLP, or computer vision concepts Experience using Jupyter notebooks, Git, or cloud platforms (AWS, GCP, Azure) Knowledge of SQL or data wrangling techniques What You’ll Gain Real-World Experience : Work on live AI projects with real business More ❯

to Have (But Not Required): Experience from a university project, hackathon, or internship involving AI or machine learning Understanding of neural networks, NLP, or computer vision concepts Experience using Jupyter notebooks, Git, or cloud platforms (AWS, GCP, Azure) Knowledge of SQL or data wrangling techniques What You’ll Gain Real-World Experience : Work on live AI projects with real business More ❯

experience in data science and statistical methods with real data problems. Advanced knowledge in Python or R coding. Significant exposure to data science and business intelligence tools (e.g., Anaconda, Jupyter NB, Databricks, R Studio). Proficiency with visualisation tools (e.g., Power BI, Tableau). Extensive knowledge of SQL and database management (e.g., SSMS, SSAS, SSRS, SSIS). Understanding of the More ❯

Python) Start-up/Scale-up Experience Strong cloud skills (AWS, GCP, Azure) and containerisation (Docker, Kubernetes) Experience in automating deployments and orchestrating cloud environments Nice to have: Python (Jupyter, PyTorch), monitoring tools (Prometheus, Grafana), cloud databases (RDS, Aurora, Spanner), CI/CD tools (CircleCI), and data visualisation experience. This is a unique opportunity to join a visionary team redefining More ❯

working as a Data Scientist or Machine Learning Engineer in a commercial setting. Strong programming skills in Python, with hands-on experience using libraries such as Pandas, Scikit-Learn, Jupyter, and Matplotlib. 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 More ❯

best practices . Apply modern software engineering standards including testing, version control, and CI/CD pipelines. Key Skills: Proven experience in Python programming for data analysis (pandas, numpy, Jupyter). Strong object-oriented programming and design pattern knowledge. Experience with SQL and relational databases. Familiarity with data visualisation tools (e.g., Plotly Dash, Power BI). Understanding of software development More ❯

best practices . Apply modern software engineering standards including testing, version control, and CI/CD pipelines. Key Skills: Proven experience in Python programming for data analysis (pandas, numpy, Jupyter). Strong object-oriented programming and design pattern knowledge. Experience with SQL and relational databases. Familiarity with data visualisation tools (e.g., Plotly Dash, Power BI). Understanding of software development More ❯

for automation. Hands-on experience with containerization & orchestration (Docker, Kubernetes, Terraform). Skilled in cloud platforms (AWS, GCP, or Azure). Bonus points for: Familiarity with ML frameworks (PyTorch, Jupyter). Knowledge of cloud monitoring tools (Prometheus, Grafana). Experience with cloud-based databases (RDS, Aurora, Redshift, Spanner, etc.). Exposure to CI/CD pipelines (e.g., CircleCI). What More ❯

best practices . Apply modern software engineering standards including testing, version control, and CI/CD pipelines. Key Skills: Proven experience in Python programming for data analysis (pandas, numpy, Jupyter). Strong object-oriented programming and design pattern knowledge. Experience with SQL and relational databases. Familiarity with data visualisation tools (e.g., Plotly Dash, Power BI). Understanding of software development More ❯

include: 3+ years industry experience in a Data Science role and a strong academic background Python Data Science Stack: Advanced proficiency in Python , including pandas , NumPy , scikit-learn , and 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 More ❯

and evaluate highly innovative models for Natural Language Programming (NLP), Large Language Model (LLM), or Large Computer Vision projects. -Use SQL to query and analyze the data. -Use Python, Jupyter notebook, and Pytorch to train/test/deploy ML models. -Use machine learning and analytical techniques to create scalable solutions for business problems. -Research and implement novel machine learning More ❯

SQL and Python Apply statistical techniques (segmentation, regression, predictive modelling) Partner with stakeholders to translate business questions into data-driven answers About You Strong experience with SQL and Python (Jupyter Notebooks) Solid grounding in statistical analysis and forecasting methods Commercially minded - able to connect technical outputs to business goals Excellent communicator, proactive and adaptable to a fast-paced environment Comfortable More ❯

SQL and Python Apply statistical techniques (segmentation, regression, predictive modelling) Partner with stakeholders to translate business questions into data-driven answers About You Strong experience with SQL and Python (Jupyter Notebooks) Solid grounding in statistical analysis and forecasting methods Commercially minded - able to connect technical outputs to business goals Excellent communicator, proactive and adaptable to a fast-paced environment Comfortable More ❯

cutting-edge ML technology across the business. Machine Learning Engineer, key skills: Significant experience working as a Machine Learning Engineer Solid knowledge of SQLandPython's ecosystem for data analysis (Jupyter, Pandas, Scikit Learn, Matplotlib). GCP, VertexAI experience is desirable (developing GCP machine learning services) Timeseries forecasting Solid understanding of computer science fundamentals, including data structures, algorithms, data modelling and More ❯

/AI/LLM Solutions. A passion for Generative AI, and an understanding of strengths and weaknesses of Generative LLM's and AI technologies. Comfortable working with Python and Jupyter Notebooks. Excellent communication skills - you can toggle seamlessly between presenting to CEOs, and getting in the weeds or white boarding with technical audiences. High tolerance for ambiguity. You can identify More ❯

Key Responsibilities Work across the development lifecycle: design, implement, and test solutions. Develop software for bare-metal MCUs, signal processing, Embedded Linux, GUI (Windows, Linux, mobile), and data analytics (Jupyter notebooks). Program in Rust, C/C++, Python, or web frameworks as required. Collaborate with FPGAs, electronic design, and mechanical teams. Work in agile teams to balance requirements, schedule … C/C++ or Rust. Knowledge of software development lifecycles, Object-Oriented Design, design patterns, and software testing. Desirable Skills GUI frameworks: eGUI, GTK, Tauri Yocto, Petalinux, uBoot Python, Jupyter, MATLAB Web development: HTML, JavaScript, TypeScript, React iOS/Android development Communication protocols: TCP, QUIC, WiFi GNU Radio, Software Defined Radios, FPGA experience UML design tools such as Enterprise Architect More ❯

Work with the Senior Engineer/Tech Lead to implement and optimise data pipelines and ML models. Develop high-quality, maintainable code in Python and PySpark. Use SQL and Jupyter to analyse, transform and validate data for ML use cases. Operate in a cloud environment (AWS/Azure - moving towards Azure). Collaborate with stakeholders and technical teams to deliver … solutions that support business growth. Skills & Experience Required: Strong hands-on experience in Python, PySpark, SQL, Jupyter . Experience in Machine Learning engineering or data-focused development. Exposure to working in cloud platforms (AWS/Azure) . Ability to collaborate effectively with senior engineers, data scientists, and architects. Proactive, detail-oriented, and eager to contribute within a greenfield project environment. More ❯