Core Data Scientist

Overview

We seek an experienced Data Scientist specializing in core aspects of data science, machine learning, natural language processing (NLP), and deep learning. The candidate should possess practical expertise in building, training, assessing, and deploying models aimed at addressing challenging business problems.

Key Responsibilities

• Design and implement comprehensive machine and deep learning models for practical business applications.
• Conduct data analysis, cleaning, feature extraction, algorithm selection, model training, and validation.
• Use statistical and mathematical methods to uncover patterns and insights from complex datasets.
• Develop NLP-based solutions including text classification, sentiment analysis, entity recognition, information extraction, text similarity, and language understanding.
• Build deep learning architectures such as CNNs, RNNs, LSTMs/GRUs, Transformers, and apply attention mechanisms.
• Handle both structured and unstructured data to create scalable and efficient data science systems.
• Optimize models through hyperparameter tuning, cross-validation, and rigorous error analysis.
• Assess models via appropriate evaluation metrics and define performance standards.
• Work collaboratively with data and ML engineers, software developers, product managers, and business teams.
• Convert business needs into analytical and model-driven solutions.
• Run experiments and proofs-of-concept converting effective methodologies into deployable solutions.
• Continuously monitor and enhance model accuracy, efficiency, and scalability in production.
• Document processes, experiments, model designs, and outcomes comprehensively.

Required Technical Proficiencies

• Deep knowledge of fundamental data science and machine learning principles.
• Proficiency in Python programming.
• Experience with ML libraries such as Scikit-learn, Pandas, NumPy, and SciPy.
• Solid grasp of both supervised and unsupervised learning algorithms.
• Familiarity with techniques including regression, classification, clustering, decision trees, random forests, gradient boosting, XGBoost, and LightGBM.
• Expertise in feature engineering and selection methods.
• Ability to conduct model evaluation and validation.
• Strong understanding of NLP theories and practical approaches.
• Hands-on experience with NLP frameworks such as NLTK, spaCy, and Hugging Face Transformers.
• Comprehensive understanding of deep learning concepts.
• Experience using TensorFlow or PyTorch frameworks.
• Knowledge of neural network types including CNN, RNN, LSTM, GRU, Transformer, and attention models.
• Firm foundation in statistical analysis, probability, linear algebra, and optimization techniques.
• Proficiency in SQL and handling extensive datasets.