20 of 20 Remote Jupyter Jobs

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 ❯

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 ❯

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 ❯

experiments (A/B tests, hypothesis testing) Communicate findings to technical and non-technical teams Create dashboards and data visualisations Use tools like Python, R, SQL, Pandas, Scikit-learn, Jupyter, Power BI, Tableau What Youll Need: Bachelors degree in Data Science, Statistics, Maths, Computer Science or related field 1+ year in a Data Scientist or similar role Strong skills in More ❯

AI/ML/Data Science apprenticeship programme. Core Skills & Competencies Technical Skills Programming proficiency in Python and common ML libraries such as TensorFlow, PyTorch, or similar. Experience with Jupyter Notebooks and version control (Git/GitHub). Basic understanding of supervised/unsupervised learning, neural networks, or clustering. Analytical Abilities Ability to interpret data trends, visualize outputs, and debug More ❯

understanding of strengths and weaknesses of Generative LLM's Fundamental knowledge of ML, and basic knowledge of AI, NLP, and Large Language Models (LLM) Comfortable working with Python and Jupyter Notebooks Should have in-depth knowledge and familiarity with cloud platforms like Amazon Web Services (AWS), Google Cloud Platform (GCP), and Microsoft Azure. Technical Skills Good to have: Expertise in More ❯

Playwright or similar testing frameworks. REST APIs: Strong understanding of integrating and working with RESTful services. Data Skills: Experience in data wrangling/analysis (e.g., using SQL or Python, Jupyter Notebook). Collaboration: Experience working in an Agile environment (Scrum/Kanban). Problem-Solving: Strong analytical and troubleshooting skills. Desirable Skills Familiarity with state management libraries (MobX, Redux). More ❯

high-performance data platforms Experience with data engineering Our current tech stack: Frontend Technologies - React, Material UI Backend & Application Frameworks - Go, Node, RabbitMQ Databases - Postgres, MariaDB, MongoDB, ClickHouse, Redis, JupyterLab, Metabase Data Engineering & Orchestration - Python, Airflow, Kafka, DataHub Cloud & Infrastructure - AWS, K8s DevOps & CI/CD - Git, GitLab CI, DBS, Grafana, ELK, Prometheus, Docker, Docker Compose Why join us? Shape More ❯

machines). • Experience working with databases (e.g., PostgreSQL, Oracle, MySQL, MongoDB, Neo4J). • Experience using version control (e.g., Git, Mercurial, SVN) to support collaborative development. • Experience utilizing notebooks (e.g., Jupyter, R Markdown, Zeppelin). • Experience developing interactive data visualizations using open-source technologies (e.g., Angular, Vue, React, D3.js) or other frameworks (e.g., Shiny). Why Join MITRE? Why choose between More ❯

zones Contributions to open-source geospatial or AI projects Experience evaluating and improving retrieval-augmented generation (RAG) pipelines (quality assessment, guardrails, and iterative improvement) Familiarity with scientific computing UX (JupyterHub, Binder, etc.) Experience engaging with the broader open-source community through talks, blogs, or forums We collaborate in the open. Clear GitHub issues, thoughtful Slack conversations, and supportive code reviews More ❯

React or similar frameworks. Provide guidance to less experienced front-end engineers. • General knowledge of machine learning and reinforcement learning concepts, frameworks, and environments, such as Pandas, TensorFlow, and Jupyter Notebook. • Broad knowledge of the general features, capabilities, and trade-offs of common data warehouse (e.g. Apache Hadoop); workflow orchestration (e.g. Apache Beam); data extract, transform and load (ETL); and More ❯

DevOps Methodologies: experience of working on Agile projects Good understanding of SOA/Microservices based architectures Good understanding of OOP, SOLID principles and software design patterns Knowledge of Python (Jupyter notebooks) Benefits offered Bonus, Pension (9% non-contributory plus additional matched contributions), 4 x Life Assurance, Group Income Protection, Season Ticket Loan, GAYE, BUPA Private Medical, Private GP, Travel Insurance More ❯

enrich data from various sources. Ensure that pipelines are automated, scalable, and fault-tolerant to accommodate large volumes of data. Experience with Notebooks, Pipelines, and Workflows: Utilise 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 ❯

Big Data solutions (ecosystems) and technologies such as: Apache Spark and the Hadoop Ecosystem Excellent knowledge of YAML or similar languages The following Technical Skills & Experience would be desirable: Jupyter Hub Awareness RabbitMQ or other common queue technology e.g. ActiveMQ NiFi Rego Familiarity with code development, shell-scripting in Python, Bash etc. To apply for this DV Cleared DevOps Engineer More ❯

and Experience required Extensive background in delivering production-grade, data-centric applications for quantitative trading and analytics, with demonstrated expertise in: The python data engineering stack (Polars, Parquet, FastAPI, Jupyter, Airflow, Streamlit, Ray) High-performance data stores and query engines (Starburst, Snowflake) Real-time streaming analytics technologies (Kafka, Flink) Cloud container technologies (AWS, Azure, GCP, Docker, Kubernetes) Proven success in 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 experience in GA4, Google Search Console, Google Tag Manager, Looker Studio, Google Cloud Console (Big Query), Google Apps Scripts Strong working knowledge of HTML, basic JavaScript, Python and Jupyter Notebooks as they relate to technical SEO analysis Proficiency in SEO audit tools such as SEMrush, Ahrefs, Screaming Frog, DeepCrawl, or similar Proficiency gathering marketing insights for analysis and reporting More ❯

certifications • Azure Synapse: Synapse-link, on-demand SQL engine, dedicated SQL pool • Writing unit tests, git Version control • Awareness of reliability patterns in ETL pipelines • Use of python in Jupyter notebooks for data processing • Azure storage technologies and cost/performance characteristics • Power BI, DAX, data flows • Techniques and tools for sanitizing data prior to use • Awareness of Kimball modelling More ❯

certifications Azure Synapse: Synapse-link, on-demand SQL engine, dedicated SQL pool Writing unit tests, git Version control Awareness of reliability patterns in ETL pipelines Use of python in Jupyter notebooks for data processing Azure storage technologies and cost/performance characteristics Power BI, DAX, data flows Techniques and tools for sanitizing data prior to use Awareness of Kimball modelling More ❯

UX development Communicate clearly and manage blockers proactively Your Profile: 1+ year professional or internship engineering experience Solid foundation in software design patterns and data structures Familiar with Git, Jupyter, command line, and agile workflows Experience with: React.js Node Python CSS Typescript Unit Testing AI/ML: LangChain, PyTorch, TensorFlow (basic understanding) Bonus: Interest in ethical AI, UX design, and More ❯