Data Science Trainer

We are seeking an experienced and passionate Data Science Trainer to join our team and deliver comprehensive training programs on Data Science, Machine Learning, Deep Learning, and related technologies. The ideal candidate will have a strong background in data science concepts, practical industry experience, and a proven ability to teach complex topics effectively. This role involves designing and conducting interactive sessions based on our structured syllabus, guiding learners through hands-on projects, and fostering a deep understanding of data-driven problem-solving. The training program spans approximately 80 hours, covering foundational to advanced topics, and emphasizes real-world applications in sectors like mobile, banking, and healthcare.

• Deliver Engaging Training Sessions

  : Conduct classroom or online sessions on core Data Science topics, including introductions to Data Science vs. Data Analytics vs. AI, project lifecycles, statistical fundamentals, Python programming, data visualization, machine learning algorithms, unsupervised learning, deep learning, natural language processing (NLP), and generative AI.

• Hands-On Demonstrations and Projects

  : Lead practical demos using tools like Jupyter, Spyder, Google Colab, and generative AI for exploratory data analysis (EDA), model building, and deployment. Guide participants through case studies such as breast cancer classification, Bangalore housing prices prediction, sales data analysis, and time series forecasting with ARIMA.

• Curriculum Alignment and Customization

  : Follow the provided syllabus to cover subtopics like probability distributions, hypothesis testing, regression models (linear, logistic), classifiers (SVM, Decision Trees), ensemble techniques (Random Forests, Gradient Boosting, XGBoost), dimensionality reduction (PCA), clustering (K-Means, DBSCAN), recommendation systems, neural networks (ANN, RNN, LSTM), text preprocessing, word embeddings (Word2Vec), and large language models (LLMs) with transformers.

• Mentor and Assess Learners

  : Provide guidance on roles in data science, important learnings, and career paths. Handle Q&A sessions, evaluate participant progress through assignments, cross-validation exercises, and final projects involving model deployment.

• Incorporate Best Practices

  : Teach data transformation techniques (scaling, encoding), model validation (K-Fold CV), handling overfitting/underfitting, regularization (Lasso, Ridge), and ethical considerations in AI/ML.

• Stay Updated and Innovate

  : Integrate emerging trends like Hugging Face libraries for LLMs and pre-trained models. Collaborate with the team to update the curriculum based on industry advancements.

• Administrative Duties

  : Prepare training materials, datasets, and code examples. Track participant engagement and provide feedback for program improvement.

• Bachelor's or Master's degree in Computer Science, Data Science, Statistics, Mathematics, or a related field. A PhD is a plus.
• Certification in Data Science, Machine Learning, or related areas (e.g., from Coursera, edX, or Google) is desirable.

• Technical Expertise

  :
• Proficiency in Python programming, including data types, control structures, loops, functions, NumPy, Pandas, Matplotlib, Seaborn, Scikit-learn, TensorFlow, Keras, and libraries for NLP (e.g., NLTK, spaCy) and generative AI (e.g., Hugging Face Transformers).
• Strong knowledge of statistics: central tendency, distributions (normal, skewness, kurtosis), charts (histograms, box plots, scatter plots), probability, hypothesis testing (Z-test, t-test), and confidence intervals.
• Expertise in Machine Learning: Supervised (regression, classification with metrics like RMSE, R², AUC, confusion matrix), unsupervised (PCA, clustering), ensemble methods (Bagging, Boosting, XGBoost, LightGBM), time series analysis (ARIMA), and recommendation systems (collaborative/content-based filtering).
• Deep Learning proficiency: Neural networks (perceptrons, backpropagation, activation functions, optimizers), RNNs, LSTMs/GRUs, and language modeling (N-grams, Word2Vec).
• NLP skills: Text preprocessing (tokenization, stemming, lemmatization), representations (BoW, TF-IDF), sentiment analysis, NER, and applications of pre-trained models.
• Familiarity with data handling: EDA, cleaning, transformation, partitioning, visualization, and deployment techniques.

• Teaching and Communication Skills

  :
• 3+ years of experience as a trainer, instructor, or mentor in Data Science/ML courses, preferably in academic or corporate settings.
• Ability to explain complex concepts simply, using real-world use cases (e.g., business problems in mobile/banking).
• Experience with interactive tools like Google Colab for in-class exercises and AI-assisted learning.

• Professional Experience

  :
• 5+ years in Data Science or related roles, with hands-on project experience in ML model development, deployment, and optimization.
• Proven track record of working on diverse datasets and case studies, including overfitting mitigation, feature engineering, and hyperparameter tuning (e.g., Grid Search CV).

• Soft Skills

  :
• Excellent presentation and interpersonal skills to engage diverse audiences.
• Problem-solving mindset with a focus on practical, industry-relevant training.
• Ability to adapt to learner needs, handle Q&A effectively, and promote collaborative learning.

• Industry experience in deploying ML models or working with big data tools.
• Publications, contributions to open-source projects, or speaking engagements in Data Science conferences.
• Familiarity with ethical AI practices and bias mitigation in models.

*** Food and Accommodation will be provided.

**** This is offline opportunity, people who are looking for online opportunity please don't apply.