Apprentice - 2

Proficient in Python programming and experienced with machine learning libraries such as Scikit-Learn and NumPy, Pandas.

Good understanding of time series analysis, data mining, text mining, and creating data architectures.

Processing Approaches: Utilize both batch processing and incremental approaches to manage and analyse large datasets. Conduct data preprocessing, feature engineering, and exploratory data analysis (EDA).

Algorithm Experimentation: Experiment with multiple algorithms, optimizing hyperparameters to identify the best-performing models.

Cloud Knowledge: Execute machine learning algorithms in cloud environments, leveraging cloud resources effectively.

Model Retraining: Continuously gather feedback from users, retrain models, and update them to maintain and improve performance, optimizing model inference times.

Network Domain Expertise: Quickly understand network characteristics, especially in RAN and CORE domains, to provide exploratory data analysis (EDA) on network data.

Transformer Architecture: Implement and utilize transformer architectures and have a strong understanding of LLM models.

Interact with a cross-functional team of data scientists, software engineers, and other stakeholders.

Understanding and experience in working with supervised and unsupervised machine learning methods such as regression, neural networks, deep learning, RNN, LSTM, KNN, Naive Bayes, SVM, decision trees, random forest, gradient boosting, ensemble methods, and text mining.

Knowledge of MySQL/No SQL and Big Data ETL Pipelines would be an added advantage