About this role
In this hourly, remote contractor role, you will work as a Jupyter Notebook Specialist (SME) to create realistic, challenging computer-based tasks using Jupyter Notebook, JupyterLab, or notebook-based data workflows. You will design practical notebook tasks, document them clearly in SuperAnnotate, ensure your setup supports the task, and record yourself executing the task on your computer.
Tasks may include creating notebooks, running Python cells, data cleaning, exploratory analysis, visualization, markdown explanations, package imports, debugging, CSV/Excel handling, simple machine learning workflows, or reproducible data-science demonstrations. Your work will help train advanced AI models to better understand how real users complete notebook-based coding and data-analysis workflows.
This role is with SME Careers, a fast-growing AI Data Services company and subsidiary of SuperAnnotate, delivering training data for many of the world’s largest AI companies and foundation-model labs. Your Jupyter Notebook expertise directly helps improve the world’s premier AI models by teaching them how real users complete practical data, coding, analysis, and documentation workflows.
Requirements
- Hands-on experience using Jupyter Notebook or JupyterLab for Python, data analysis, visualization, teaching, research, automation, or documentation workflows.
- English proficiency at B2 level or higher to understand project instructions, document tasks clearly, and communicate with the project team.
- Comfortable using a computer, managing files, running notebooks, installing packages where needed, using browser/local environments, and recording workflows.
- Ability to come up with realistic and challenging Jupyter Notebook tasks that reflect actual data analysis, coding, education, research, reporting, or technical documentation use cases.
- Strong understanding of notebook basics such as code cells, markdown cells, execution order, kernels, imports, file paths, pandas, charts, outputs, and exports.
- Ability to ensure your setup supports the task, including having Jupyter available, files/datasets prepared, packages installed, and any required references ready before recording.
- High attention to detail when preparing the task, executing the workflow, and checking that the recording is complete and understandable.
- Reliable, self-directed, and able to deliver consistent quality in an hourly, remote contractor workflow across time zones.
- Additional professional skills are appreciated, including Python, data analysis, machine learning, statistics, research, teaching, coding, reporting, or other computer-based workflows.
Responsibilities
- Create realistic Jupyter Notebook tasks: Come up with realistic and challenging tasks that can be executed on the computer using Jupyter Notebook or JupyterLab, then document the task clearly in SuperAnnotate.
- Prepare the work environment: Ensure your setup supports the task, including having Jupyter available, datasets prepared, packages installed, notebooks created, and any required references ready before recording.
- Record task execution: Record yourself executing the Jupyter Notebook task on your computer while following project instructions and ensuring the workflow is clear, complete, and realistic.
- Document task context: Provide enough task context, steps, goals, files, dataset details, and notes so reviewers can understand what the task is and how it was completed.
- Demonstrate practical software use: Complete workflows involving Python cells, markdown explanations, data cleaning, pandas, charts, debugging, imports, file handling, notebook outputs, or exports where relevant.
- Maintain submission quality: Review your recording and documentation before submission to ensure the task is complete, reproducible, understandable, realistic, and aligned with project guidelines.
Skills
- Pandas
- Python
- Data Science
- Data Analysis
- Visualization
- Screen Recording
- JupyterLab
- Computer Use
- English
- Jupyter Notebook
Additional skills
- Jupyter Notebook
- JupyterLab
- Python
- IPython
- Anaconda
- pip
- conda
- Virtual Environments
- Notebook Kernels
- Markdown
- pandas
- NumPy
- Matplotlib
- Seaborn
- Plotly
- Data Visualization
- Data Analysis
- Exploratory Data Analysis (EDA)
- Data Cleaning
- Statistical Analysis
- Machine Learning
- scikit-learn
- Data Wrangling
- File I/O
- CSV
- JSON
- SQL
- Git
- Technical Documentation
- Reproducible Research
