a. Statistics & Probability Hypothesis testing, p-values, confidence intervals Descriptive stats: mean, median, mode, variance, skewness, kurtosis Probability distributions: Normal, Poisson, Binomial Bayes’ Theorem and conditional probabilities b. Machine Learning Supervised vs. unsupervised learning Model evaluation metrics (e.g. precision, recall, AUC-ROC, confusion matrix) Algorithms: linear/logistic regression, decision trees, random forest, SVM, k-NN, clustering (K-means, DBSCAN), PCA Bias-variance trade-off, overfitting/underfitting, cross-validation c. Deep Learning (if relevant) Neural network basics (activation functions, backpropagation) CNNs, RNNs/LSTMs for NLP or image data (if role-specific) Frameworks: TensorFlow, PyTorch d. Coding & Data Manipulation Python (pandas, NumPy, scikit-learn, matplotlib/seaborn) SQL: Joins, subqueries, window functions, CTEs

I recently applied for a position directly through the company's career site and was able to connect with the hiring manager. Throughout the process, I underwent one HR interview, two technical interviews, and one behavioral interview. The first technical interview covered topics in deep learning, machine learning, and generative AI, while the second focused on data engineering, specifically SQL, Python, and Spark. Compared to previous experiences, this hiring process stood out as the best. The interviewers were welcoming, the questions were pertinent, and the entire process was efficiently managed, concluding with an offer within just three weeks of the initial interview.