Ability to create efficient solutions to complex problems. Strong skills in data-structures and ML algorithms. Experience in working on end-to-end data science pipeline: problem scoping, data gathering, EDA, modeling, insights, visualizations, monitoring, and maintenance. Problem-solving: Ability to break the problem into small parts and applying relevant techniques to drive required outcomes. Intermediate to advanced knowledge of machine learning, probability theory, statistics, and algorithms. You will be required to discuss and use various algorithms and approaches on a daily basis. We use regression, Bayesian methods, tree-based learners, SVM, RF, xgboost, time series modeling, dimensionality reduction, SEM, GLM, GLMM, clustering, deep learning, etc. on a regular basis. Deep knowledge of maths, probability, statistics, and algorithms Proficiency with various techniques in ML such as Regression, Classification, Forecasting, and Cluster Analysis Techniques: Regression analysis, Natural Language Processing (NLP) models, CNN DNN, Decision tree, Deep learning algorithms. advanced programming C++: CUDA, OpenMP CGAL, Python: Pandas, NumPy, SciPy, PySpark, PyTorch, Matplotlib, TensorFlow, Keras, PyTorch.