of computer science fundamentals, including data structures, algorithms, data modeling, and software architecture. Proficiency in classical machine learning algorithms (e.g., Logistic Regression, Random Forest, XGBoost) and modern deep learning algorithms (e.g., BERT, LSTM). Strong knowledge of SQL and Python's data analysis ecosystem (Jupyter, Pandas, Scikit-Learn, Matplotlib). more »
of computer science fundamentals, including data structures, algorithms, data modelling, and software architecture. Proficiency in classical machine learning algorithms (eg, Logistic Regression, Random Forest, XGBoost) and modern deep learning algorithms (eg, BERT, LSTM). Strong knowledge of SQL and Python's data analysis ecosystem (Jupyter, Pandas, Scikit-Learn, Matplotlib). more »
of computer science fundamentals, including data structures, algorithms, data modelling and software architecture Strong knowledge of Machine Learning algorithms (e.g. Logistic Regression, Random Forest, XGBoost, etc.) as well as state-of-the-art research area (e.g. NLP, Transfer Learning etc.) and modern Deep Learning algorithms (e.g. BERT, LSTM, etc.) Machine more »
building using R and/or Python languages (generalized linear models, logistics/linear Regression, Tree based and Boosting models, Decision trees, Random Forest, XGBoost etc.- Basic understanding of Neural network and deep learning models). Possess experience with Machine learning/Pattern recognition areas. Should possess basic Data Engineering more »
hands-on experience in machine learning engineering. Proven expertise in regression modelling and time series modelling. Numpy/Pandas/Keras/Tensorslow/XGBoost/Scikit-learn Experienced in GCP preferably Extensive background in deploying and productionizing machine learning models. Strong programming skills in languages such as Python, R more »
