18 of 18 Jupyter Jobs in England

to strong results. Experienced in engaging with non-technical partners to scope, design and build an appropriate ML solution. Proficient with Python data science stack, e.g., pandas, scikit-learn, Jupyter etc., and version control, e.g., Git. Knowledge of OO programming, software design, i.e., SOLID principles, and testing practices. Knowledge and working experience of AGILE methodologies. Proficient with SQL. Familiarity with More ❯

to strong results. Experienced in engaging with non-technical stakeholders to scope, design and build an appropriate ML solution. Proficient with Python data science stack, e.g., pandas, scikit-learn, Jupyter etc., and version control, e.g., Git. Exposure to LLMOps, model monitoring principles, CI/CD and associated tech, e.g., Docker, MLflow, k8s, FastAPI etc. Knowledge of Langchain, huggingface and LLM More ❯

knowledge of React (not a frontend role, but an understanding of the stack is important) Hands-on experience with containerisation (Docker) and cloud deployment (Terraform, microservices, Azure) Exposure to Jupyter notebooks , and understanding of how machine learning models are developed and deployed Experience in fast-paced or start-up environments where you’ve contributed across the stack Background & Education Degree More ❯

knowledge of React (not a frontend role, but an understanding of the stack is important) Hands-on experience with containerisation (Docker) and cloud deployment (Terraform, microservices, Azure) Exposure to Jupyter notebooks , and understanding of how machine learning models are developed and deployed Experience in fast-paced or start-up environments where you’ve contributed across the stack Background & Education Degree More ❯

Chat or Chatbors, particular voice, is a must-have. Strong recent hands-on capabilities in Python are a must-have. Experience with a microservice environment using tools such as Jupyter, Pandas, Numpy, Fast API, and SQL alchemy is a must. Srong's understanding of deploying and supporting LLM’s in a production environment is a must-have. An interest and More ❯

into scalable solutions. Essential Experience & Skills 5+ years' engineering experience, with at least 3 years in ML Ops, Data Engineering, or AI infrastructure. Strong Python engineering skills (Pandas, Numpy, Jupyter, FastAPI, SQLAlchemy). Expertise in AWS services (certification desirable). Proven experience deploying and supporting LLMs in production. Strong understanding of LLM fine-tuning (PyTorch, TensorFlow, Hugging Face Trainer, etc. More ❯

cloud platforms (AWS, GCP, Azure). Proficiency in containerization and orchestration (Docker, Kubernetes). Expertise in managing and optimizing cloud-based systems at scale. Preferred Skills Familiarity with Python (Jupyter) and ML frameworks (e.g., PyTorch). Knowledge of monitoring tools (Prometheus, Grafana). Experience with cloud-based databases (RDS, Aurora, Redshift, Cloud SQL, etc.) and visualization tools (QuickSight, Superset). 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 ❯

etc.), state-of-the-art areas (e.g. NLP, Transfer Learning) and modern Deep Learning algorithms (e.g. BERT, LSTM) Solid knowledge of SQL and Python's ecosystem for data analysis (Jupyter, Pandas, Scikit-Learn, Matplotlib, etc.) Understanding of model evaluation, data pre-processing techniques (standardisation, normalisation, handling missing data) Solid understanding of statistics; hypothesis testing, probability distributions, sampling techniques Private Health 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 ❯

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 ❯

software correctness Bonus Points Contributed code to open-source projects in the Web3 space Experience developing DeFi trading tools or frameworks Proficiency with Python scientific computing stack (NumPy, Pandas, Jupyter) Smart contract programming experience in Solidity Programming experience in a systems language like Go, Rust, or C++ Our compensation is blockchain competitive and dependent on your skills and experience. More ❯

pipelines to clean, transform, and enrich data from various sources. Ensure that pipelines are automated, scalable, and fault-tolerant to accommodate large volumes of data. Experience with Notebooks (e.g., Jupyter, Databricks) for data exploration, analysis, and reporting. Design and optimise data workflows to streamline key processing tasks, enhancing operational efficiency. API Integration & Data Ingestion: Integrate external and internal APIs to More ❯

technology across the business. Machine Learning Engineer, key skills: Significant experience working as a Data Scientist/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 ❯

be eligible for SC Clearance A strong understanding of secure data practices in government or similarly regulated environments Practical experience building and managing Python-based analytical tooling environments (e.g. JupyterHub, VS Code in-browser) Demonstrable experience developing end-to-end data pipelines, including ingestion, transformation, validation, and output layers Familiarity with best practices in CI/CD, infrastructure-as-code More ❯

and dedicated time for your personal development What you'll be working with: •Backend: Distributed, event-driven core Java (90% of the code-base), MySQL, Kafka •Data analytics: Python & Jupyter notebooks, Parquet, Docker •Testing: JUnit, JMH, JCStress, Jenkins, Selenium, many in-house tools •OS: Linux (Fedora for development, Rocky in production) The LMAX way is to use the right tool More ❯

power dashboards and applications You'll integrate third-party APIs and databases into our data flows You'll perform in-depth analysis and data transformations with SQL, Python, and Jupyter Notebooks You'll prototype internal data applications and tools (Streamlit, Jupyter) You'll ensure data quality and reliability throughout the lifecycle You'll collaborate with product, technology, and strategy teams … to visualisation and presentation. Tool Building: Passion for creating robust and usable tools to enhance team efficiency. Data Analysis Skills: Proficiency in SQL, python, pandas/numpy, Databricks/Jupyter Notebooks, and experience with large datasets in production environments. Curiosity and Drive: Self-motivated with a strong desire to learn and improve without lots of help and support. Growth mindset … is an excellent start What We Work With dbt for data modelling Databricks Delta Lake for data lake and warehouse storage and querying Python as our main programming language Jupyter and Jupyter Hub for notebook analytics and collaboration Circle CI for continuous deployment AWS cloud infrastructure Kubernetes for data services and task orchestration Google Analytics, Amplitude and Firebase for client More ❯

scripts and visualizations to explore and summarize biological experiment data. Apply statistical techniques (e.g. descriptive statistics, t-tests, ANOVA, regression) to help interpret experiments Create reproducible analysis workflows using Jupyter, Python, and associated libraries. Work with scientists to understand experimental goals and tailor analyses accordingly. Document analyses and communicate findings in a clear, concise manner. Collaborate with the data engineering … statistics: distributions, hypothesis testing, confidence intervals, regression. Experience working with structured datasets Ability to produce clear, well-commented code and visual outputs (e.g. plots, dashboards). Comfortable working in Jupyter Notebooks and collaborating with experimental teams. A degree (BSc or MSc) in a quantitative field such as Biology, Bioinformatics, Statistics, Data Science, or related discipline. There is an excellent salary More ❯