Machine Learning Specialist

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Role Overview

Advanced Machine Learning Tutor

This role is perfect for ML professionals who love teaching, enjoy simplifying complex ideas, and want to make a real impact on career-oriented learners.

Key Responsibilities

• Deliver

  live weekend sessions

  on advanced ML concepts, such as:
• Ensemble Methods (Bagging, Boosting, Stacking)
• Feature Engineering and Selection Techniques
• Model Evaluation & Validation (Cross-Validation, ROC-AUC, etc.)
• Unsupervised Learning (PCA, Clustering techniques like K-Means, DBSCAN)
• Time Series Forecasting (ARIMA, Prophet, LSTM basics)
• Hyperparameter Tuning (GridSearchCV, RandomizedSearch, Bayesian Optimization)
• Introduction to Deep Learning (optional, depending on scope)
• Guide students through

  project-based learning

  using real-world datasets.
• Support students with

  doubts, debugging, and tool usage

  (like scikit-learn, XGBoost, LightGBM).
• Collaborate on

  curriculum refinement

  and improve content delivery.
• Keep updated with current trends in ML and bring them into your teaching.

Required Qualifications

• Strong experience in

  Machine Learning beyond basic models

• Proficient with Python, NumPy, pandas, scikit-learn, XGBoost, etc.
• Familiar with advanced concepts like model selection, bias-variance tradeoff, overfitting/underfitting solutions
• Ability to explain and mentor real-world ML projects
• Previous teaching, tutoring, or mentoring experience (preferred)
• Excellent communication and presentation skills

Nice to Have

• Familiarity with MLOps basics and tools (like MLflow)
• Experience with real-time or production ML systems
• Exposure to research papers or Kaggle competitions
• Understanding of Time Series and basic DL models like LSTMs
• Prior educational or ed-tech experience

Students Will Learn To:

• Build and fine-tune complex ML models
• Evaluate model performance using advanced techniques
• Work with imbalanced datasets and noise
• Engineer features for better accuracy
• Solve real-world problems (credit risk, fraud detection, forecasting, etc.)