19 of 19 Remote Jupyter Jobs

Services (S3, EKS, ECR, EMR, etc.) •Experience with containers and orchestration (e.g. Docker, Kubernetes) •Experience with Big Data processing technologies (Spark, Hadoop, Flink etc) •Experience with interactive notebooks (e.g. JupyterHub, Databricks) •Experience with Git Ops style automation •Experience with ix (e.g, Linux, BSD, etc.) tooling and scripting •Participated in projects that are based on data science methodologies, and/or 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 ❯

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 ❯

and model evaluation Requirements: 3+ years of experience in data science or ML, ideally in biotech or healthcare Strong Python programming skills and experience with ML libraries Familiarity with Jupyter , Pandas , NumPy , and MLFlow Experience working with clinical or biological datasets is a big plus Comfortable working in a fast-paced, research-driven environment Bonus Skills: Knowledge of genomics , bioinformatics More ❯

and model evaluation Requirements: 3+ years of experience in data science or ML, ideally in biotech or healthcare Strong Python programming skills and experience with ML libraries Familiarity with Jupyter , Pandas , NumPy , and MLFlow Experience working with clinical or biological datasets is a big plus Comfortable working in a fast-paced, research-driven environment Bonus Skills: Knowledge of genomics , bioinformatics 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 ❯

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 ❯

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 ❯

retrieval methods. • Knowledgeable in various synthetic data generation approaches, such as GANs, VAEs, or rule-based systems. • Experience writing unit tests for ML models and associated components. • Knowledgeable with Jupyter notebooks and Python development. • Experience cleaning and preparing structured datasets for training and analysis. • Experience creating basic features to enhance data utility for machine learning models. • Knowledgeable in using predefined More ❯

and other Qualtrics products Acquire data from customers (usually sftp or cloud storage APIs) Validate data with exceptional detail orientation (including audio data) Perform data transformations (using Python and Jupyter Notebooks) Load the data via APIs or pre-built Discover connectors Advise our Sales Engineers and customers as needed on the data, integrations, architecture, best practices, etc. Build new AWS 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 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 ❯

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 ❯

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 ❯

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 ❯

written communication skills, with the ability to explain data findings to both technical and non-technical audiences. Experience delivering data driven insights to businesses. Familiarity with tools such as Jupyter Notebook and basic Python for data analysis. Some exposure to cloud platforms (e.g., AWS, GCP, or Azure) and interest in learning cloud-based data tools. Experience in using BI tools 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 ❯

cleaning, and automation Ability to turn data into stories using tools like Looker Studio, Power BI, or Tableau Comfortable working with cloud-based platforms and spreadsheets (e.g., GCP BigQuery, Jupyter notebooks, Google Sheets) Understanding of A/B testing, regression models, and evaluation methods More ❯