Data Science - Trainer - Part-Time - Remote

Job Title: Data Science Trainer Part-Time | Remote

Job Type: Part-Time / Freelance

Work Mode: Remote / Online

Role: Data Science Trainer

Training Audience: Students and working professionals in the USA

We are looking for an experienced Data Science Trainer to deliver live, instructor-led online Data Science training to students and working professionals in the USA.

Important:
This is a Data Science Trainer role and NOT a full-time Data Scientist position.

The ideal candidate should have strong hands-on Data Science experience and the ability to explain technical concepts through practical demonstrations, hands-on labs, real-world datasets, projects, and industry-oriented scenarios.

• Deliver live online Data Science training to USA-based students and working professionals.
• Strong Statistical and Machine Learning modeling experience
• Advanced programming skills; mastery of a statistical language such as R or SAS; experience using other programming and data manipulation languages (SQL, Hive, Pig, Python, C/C++, Java); familiarity with relational, MPP, and/or Hadoop data management frameworks; proficiency with Microsoft Office tools
• Fluency in at least one deep-learning framework (PyTorch is strongly preferred).
• Train students on Python, Num Py, Pandas, SQL, Statistics, Data Analysis, Data Visualization, and Machine Learning.
• Provide hands-on training on Python programming, Jupyter/Colab, data manipulation, data cleaning, exploratory data analysis, and SQL-based data analysis.
• Train students on Matplotlib, Seaborn, dashboard basics, visualization techniques, and stakeholder-oriented data storytelling.
• Cover probability, distributions, confidence intervals, hypothesis testing, p-values, effect sizes, and A/B testing.
• Train students on Machine Learning fundamentals, feature engineering, preprocessing, train/validation/test splits, cross-validation, model evaluation, and ML pipelines.
• Provide practical training on Regression, Classification, Decision Trees, Random Forests, Boosting concepts, Clustering, K-Means, and PCA.
• Explain machine learning evaluation techniques including MAE, RMSE, precision, recall, ROC-AUC, calibration, error analysis, and model validation.
• Train students on data leakage, class imbalance, feature importance, model interpretation, bias/fairness checks, and reproducible machine learning workflows.
• Provide exposure to Scikit-learn, Git, Git Hub, Streamlit, Jupyter Notebook, Google Colab, and other relevant Data Science tools.
• Guide students in developing real-world Data Science projects, dashboards, applications, and Git Hub portfolios.
• Conduct hands-on labs, weekly assignments, assessments, project reviews, and technical discussions.
• Mentor students throughout the capstone project, including problem definition, data preparation, EDA, statistical analysis, model development, validation, documentation, and presentation.
• Guide students on presenting their projects and explaining technical decisions during Data Science interviews.
• Adapt teaching methods according to different student skill levels and provide constructive feedback throughout the program.

• Minimum 5+ years of professional experience in Data Science
• Strong hands-on experience with Python, Pandas, Num Py, SQL, Statistics, Data Visualization, and Machine Learning.
• Strong practical experience with Scikit-learn, Matplotlib, Seaborn, Jupyter Notebook / Google Colab, Git/Git Hub, and related Data Science tools.
• Good knowledge of data cleaning, EDA, statistical analysis, hypothesis testing, feature engineering, model development, model evaluation, and machine learning workflows.
• Experience working with Regression, Classification, Decision Trees, Random Forests, Clustering, K-Means, PCA, and related machine learning techniques.
• Strong understanding of train/validation/test splits, cross-validation, data leakage, model evaluation metrics, error analysis, and reproducibility.
• Experience building or mentoring end-to-end Data Science projects using real-world datasets.
• Experience with Streamlit or similar tools for creating simple Data Science applications is preferred.
• Strong communication and presentation skills.
• Ability to explain complex Data Science concepts in an easy-to-understand and practical manner.
• Previous experience in training, mentoring, teaching, bootcamps, corporate training, or technical knowledge transfer is highly preferred.
• Experience handling US-based students, professionals, or clients is preferred.
• Must…