Data Science Trainer

Role Overview

This position centers on guiding adult learners through the complete data science workflow, from first queries in SQL to deploying and interpreting machine learning models. Day to day, the trainer will alternate between interactive lectures, live coding demonstrations, and one-on-one mentoring during lab sessions. The work matters because graduates need not only theoretical knowledge but also the practical judgment to source, clean, analyze, and present data in a way that drives business decisions — and the trainer is the person who makes that happen.

Key Responsibilities

• Deliver structured lessons covering Python programming, SQL for data extraction, statistical reasoning, data cleaning, exploratory analysis, and core machine learning algorithms.
• Design and lead hands-on lab sessions where learners work directly with messy, real-world datasets to practice their skills in a controlled but realistic environment.
• Guide learners through end-to-end portfolio projects, helping them frame business questions, select appropriate techniques, and present findings with visual clarity.
• Evaluate homework, project milestones, and final submissions using clear rubrics, then provide actionable feedback that connects technical quality with career readiness.
• Adapt teaching pace and examples based on learner backgrounds, ensuring that both beginners and more experienced students stay challenged and engaged.
• Keep course materials, exercise notebooks, and assessment criteria current with evolving industry tools and practices.
• Hold office hours or dedicated Q&A sessions to troubleshoot coding problems, discuss statistical concepts, and support learners who need extra help.
• Collaborate with curriculum leads or program managers to refine learning outcomes and identify gaps in the student experience.

Requirements & Qualifications

• Bachelor's degree (or higher) in Data Science, Statistics, Computer Science, Mathematics, or a closely related quantitative field.
• At least two years of hands-on experience applying data analysis and machine learning in a professional or research setting.
• Strong command of Python and SQL, including pandas for data manipulation, Matplotlib/Seaborn for visualization, and scikit-learn or similar libraries for modeling.
• Solid grounding in core statistical concepts — hypothesis testing, regression, confidence intervals, and model evaluation — with the ability to translate these into plain-language explanations.
• Demonstrated ability to break down complex technical subjects into digestible steps, whether through code-alongs, diagrams, or analogies.
• Prior experience in teaching, tutoring, mentoring, or leading technical workshops is strongly preferred.
• A portfolio or GitHub repository showcasing original data analysis, visualization, or machine learning projects that reflect clear communication of findings.