1 to 25 of 147 Jupyter Jobs in England

following database systems – DynamoDB, DocumentDB, MongoDB Demonstrated expertise in unit testing and tools – JUnit, Mockito, PyTest, Selenium. Strong working knowledge of the PyData stack – pandas, NumPy for data manipulation; Jupyter Notebooks for experimentation; matplotlib/Seaborn for basic visualisation. Experience with data analysis and troubleshooting data-related issues. Knowledge of design patterns and software architectures Familiarity with CI/CD … following database systems – DynamoDB, DocumentDB, MongoDB Demonstrated expertise in unit testing and tools – JUnit, Mockito, PyTest, Selenium. Strong working knowledge of the PyData stack – pandas, NumPy for data manipulation; Jupyter Notebooks for experimentation; matplotlib/Seaborn for basic visualisation. Experience with data analysis and troubleshooting data-related issues. Knowledge of design patterns and software architectures Familiarity with CI/CD More ❯

following database systems – DynamoDB, DocumentDB, MongoDB Demonstrated expertise in unit testing and tools – JUnit, Mockito, PyTest, Selenium. Strong working knowledge of the PyData stack – pandas, NumPy for data manipulation; Jupyter Notebooks for experimentation; matplotlib/Seaborn for basic visualisation. Experience with data analysis and troubleshooting data-related issues. Knowledge of design patterns and software architectures Familiarity with CI/CD More ❯

areas: Big Data Analytics (e.g. Google BigQuery/BigTable, Apache Spark), Parallel Computing (e.g. Apache Spark, Kubernetes, Databricks), Cloud Engineering (AWS, GCP, Azure), Spatial Query Optimisation, Data Storytelling with (Jupyter) Notebooks, Graph Computing, Microservices Architectures ● Modelling & Statistical Analysis experience, ideally customer related ● A university degree – numbers based, Computer Science or Geography ● Relevant industry sector knowledge ideal but not essential ● A More ❯

in building machine learning models for tasks like recommendations, segmentation, forecasting, and optimising marketing spend. Proficiency in Python, SQL, Bash, and Git, with hands-on experience in tools like Jupyter notebooks, Pandas, PyTorch, and more. Experience with A/B testing and other experimentation methods to validate model performance and business impact. Experience with cloud platforms (AWS, Databricks, Snowflake), containerisation More ❯

and interpreting large datasets. Ability to apply statistical techniques to validate models and algorithms. Data Manipulation & Analysis: Proficient in data manipulation and analysis using tools like Pandas, NumPy, and Jupyter Notebooks. Experience with data visualization tools such as Matplotlib, Seaborn, or Tableau to communicate insights effectively. Our offer: Cash: Depends on experience Equity: Generous equity package, on a standard vesting More ❯

management readiness. Proven track record to take research from concept to business impact. Strong Python toolkit proficiency for Data Science, with experience in SQL and NoSQL databases. Familiarity with Jupyter Notebooks and Git version control. Expertise in working with large, sophisticated datasets and extracting actionable insights. Project management experience with tight deadlines. Ability to work independently and take ownership of More ❯

management readiness. Proven track record to take research from concept to business impact. Strong Python toolkit proficiency for Data Science, with experience in SQL and NoSQL databases. Familiarity with Jupyter Notebooks and Git version control. Expertise in working with large, sophisticated datasets and extracting actionable insights. Project management experience with tight deadlines. Ability to work independently and take ownership of More ❯

a related field. Proven experience in machine learning applications such as recommendation systems, segmentation, and marketing optimisation. Proficiency in Python, SQL, Bash, and Git, with hands-on experience in Jupyter notebooks, Pandas, and PyTorch. Familiarity with cloud platforms (AWS, Databricks, Snowflake) and containerisation tools (Docker, Kubernetes). Strong problem-solving skills and a passion for driving measurable business impact. Knowledge More ❯

a related field. Proven experience in machine learning applications such as recommendation systems, segmentation, and marketing optimisation. Proficiency in Python, SQL, Bash, and Git, with hands-on experience in Jupyter notebooks, Pandas, and PyTorch. Familiarity with cloud platforms (AWS, Databricks, Snowflake) and containerisation tools (Docker, Kubernetes). Strong problem-solving skills and a passion for driving measurable business impact. Knowledge More ❯

learning models Build AI systems using Large Language Models Build processes for extracting, cleaning and transforming data (SQL/Python) Ad-hoc data mining for insights using Python + Jupyter notebooks Present insights and predictions in live dashboards using Tableau/PowerBI Lead the presentation of findings to clients through written documentation, calls, and presentations Actively seek out new opportunities More ❯

improve our ability to serve clients. Tech Skills Required: Advanced level of coding in Python for Data Science Software engineering architecture design for application with integrated Data Science solutions Jupyter server/notebooks AWS: EC2, Sagemaker, S3 Git version control SQL skills include selecting, filtering, aggregating, and joining data using core clauses, use of CTEs, window functions, subqueries, and data More ❯

learning models Build AI systems using Large Language Models Build processes for extracting, cleaning and transforming data (SQL/Python) Ad-hoc data mining for insights using Python + Jupyter notebooks Present insights and predictions in live dashboards using Tableau/PowerBI Lead the presentation of findings to clients through written documentation, calls and presentations Actively seek out new opportunities More ❯

and dashboards to track key metrics and support decision-making Collaborate with product, engineering, and business teams to translate goals into data projects Use tools like Python, R, SQL, Jupyter, Pandas, Scikit-learn, TensorFlow, Power BI, Tableau , and others Requirements Minimum Bachelor's degree in Data Science, Mathematics, Statistics, Computer Science, or a related field Proven experience as a Data More ❯

a related field. Proven experience in machine learning applications such as recommendation systems, segmentation, and marketing optimisation. Proficiency in Python, SQL, Bash, and Git, with hands-on experience in Jupyter notebooks, Pandas, and PyTorch. Familiarity with cloud platforms (AWS, Databricks, Snowflake) and containerisation tools (Docker, Kubernetes). Strong problem-solving skills and a passion for driving measurable business impact. Knowledge More ❯

design and deployment. Strong software engineering skills, including version control (Git), code reviews, and unit testing. Familiarity with common data science libraries and tools (e.g., NumPy, Pandas, Scikit-learn, Jupyter). Experience in setting up and managing continuous integration and continuous deployment pipelines. Proficiency with containerization technologies (e.g., Docker, Kubernetes). Experience with cloud services (e.g., AWS, GCP, Azure) for More ❯

design and deployment. Strong software engineering skills, including version control (Git), code reviews, and unit testing. Familiarity with common data science libraries and tools (e.g., NumPy, Pandas, Scikit-learn, Jupyter). Experience in setting up and managing continuous integration and continuous deployment pipelines. Proficiency with containerization technologies (e.g., Docker, Kubernetes). Experience with cloud services (e.g., AWS, GCP, Azure) for More ❯

storage and retrieval. Strong software engineering skills, including version control (Git), code reviews, and unit testing. Familiarity with common data science libraries and tools (e.g., NumPy, Pandas, Scikit-learn, Jupyter). Experience in setting up and managing continuous integration and continuous deployment pipelines. Proficiency with containerization technologies (e.g., Docker, Kubernetes). Experience with cloud services (e.g., AWS, GCP, Azure) for More ❯

storage and retrieval. Strong software engineering skills, including version control (Git), code reviews, and unit testing. Familiarity with common data science libraries and tools (e.g., NumPy, Pandas, Scikit-learn, Jupyter). Experience in setting up and managing continuous integration and continuous deployment pipelines. Proficiency with containerization technologies (e.g., Docker, Kubernetes). Experience with cloud services (e.g., AWS, GCP, Azure) for More ❯

and product managers. You can evaluate, analyze and interpret model results resulting in further improvement of existing statistical model performance You can perform complex data analysis using SQL/Jupyter notebook to find underlying issues and propose a solution to stakeholders explaining the various trade-offs associated with the solution. You can use your grit and initiative to fill in More ❯

testing frameworks (e.g., DoWhy, causalml) Programming & Data Tools : Python: Strong foundation in Pandas, NumPy, matplotlib/seaborn, scikit-learn, TensorFlow, Pytorch etc. SQL: Advanced querying for large-scale datasets. Jupyter, Databricks, or notebooks-based workflows for experimentation. Data Access & Engineering Collaboration : Comfort working with cloud data warehouses (e.g., Snowflake, Databricks, Redshift, BigQuery) Familiarity with data pipelines and orchestration tools like More ❯

testing frameworks (e.g., DoWhy, causalml) Programming & Data Tools : Python: Strong foundation in Pandas, NumPy, matplotlib/seaborn, scikit-learn, TensorFlow, Pytorch etc. SQL: Advanced querying for large-scale datasets. Jupyter, Databricks, or notebooks-based workflows for experimentation. Data Access & Engineering Collaboration : Comfort working with cloud data warehouses (e.g., Snowflake, Databricks, Redshift, BigQuery) Familiarity with data pipelines and orchestration tools like More ❯

testing frameworks (e.g., DoWhy, causalml) Programming & Data Tools : Python: Strong foundation in Pandas, NumPy, matplotlib/seaborn, scikit-learn, TensorFlow, Pytorch etc. SQL: Advanced querying for large-scale datasets. Jupyter, Databricks, or notebooks-based workflows for experimentation. Data Access & Engineering Collaboration : Comfort working with cloud data warehouses (e.g., Snowflake, Databricks, Redshift, BigQuery) Familiarity with data pipelines and orchestration tools like More ❯

testing frameworks (e.g., DoWhy, causalml) Programming & Data Tools : Python: Strong foundation in Pandas, NumPy, matplotlib/seaborn, scikit-learn, TensorFlow, Pytorch etc. SQL: Advanced querying for large-scale datasets. Jupyter, Databricks, or notebooks-based workflows for experimentation. Data Access & Engineering Collaboration : Comfort working with cloud data warehouses (e.g., Snowflake, Databricks, Redshift, BigQuery) Familiarity with data pipelines and orchestration tools like More ❯

testing frameworks (e.g., DoWhy, causalml) Programming & Data Tools : Python: Strong foundation in Pandas, NumPy, matplotlib/seaborn, scikit-learn, TensorFlow, Pytorch etc. SQL: Advanced querying for large-scale datasets. Jupyter, Databricks, or notebooks-based workflows for experimentation. Data Access & Engineering Collaboration : Comfort working with cloud data warehouses (e.g., Snowflake, Databricks, Redshift, BigQuery) Familiarity with data pipelines and orchestration tools like More ❯

testing frameworks (e.g., DoWhy, causalml) Programming & Data Tools : Python: Strong foundation in Pandas, NumPy, matplotlib/seaborn, scikit-learn, TensorFlow, Pytorch etc. SQL: Advanced querying for large-scale datasets. Jupyter, Databricks, or notebooks-based workflows for experimentation. Data Access & Engineering Collaboration : Comfort working with cloud data warehouses (e.g., Snowflake, Databricks, Redshift, BigQuery) Familiarity with data pipelines and orchestration tools like More ❯