Sr. Data Scientist

You will be responsible for applying Natural Language Processing (NLP) AI techniques, conducting machine learning, and developing high-quality prediction systems for data classification. Your main tasks will include presenting information through data visualization techniques, as well as collecting, preprocessing, and harmonizing data. As a part of your role, you will develop applications in machine learning and artificial intelligence, while selecting features, building, and optimizing classifiers using machine learning techniques. Understanding business objectives and creating models to support them, along with relevant metrics to monitor progress, will be crucial. You will need to manage available resources efficiently, such as hardware, data, and personnel, to ensure project deadlines are met. Your responsibilities will also involve analyzing various ML algorithms to identify the most suitable ones for solving a specific problem and ranking them based on success probability. Exploring and visualizing data to comprehend it better, detecting variations in data distribution that could impact model performance in real-world deployment, and verifying data quality through cleaning processes will be essential. You will supervise the data acquisition process if additional data is required, search for relevant datasets online for training purposes, define validation strategies, and determine pre-processing or feature engineering on datasets. Furthermore, you will be involved in defining data augmentation pipelines, training models, tuning hyperparameters, analyzing model errors, and developing strategies to address them. Deployment of models to production environments will also be a key aspect of your role. As a desired candidate, you should possess a strong understanding of machine learning (ML) and deep learning (DL) algorithms, as well as an architectural comprehension of CNN and RNN algorithms. Experience with NLP data models and libraries, entity extraction using NLP, and proficiency in tools like TensorFlow, scikit-learn, and spaCy is required. Additionally, familiarity with transfer learning, scripting and programming skills in Python and Streamlit, common data science toolkits such as NumPy and Fast.AI, and various machine learning techniques and algorithms like k-NN, Naive Bayes, and SVM is essential. Proficiency in query languages, applied statistics skills, data wrangling, data exploration, as well as tools like Tableau and DataPrep will be advantageous. The ideal candidate will be an immediate joiner with no restrictions on salary for the right individual.,