Core Data Scientist

We are looking for a highly skilled Data Scientist with strong expertise in Core Data Science, Machine Learning, Natural Language Processing (NLP), and Deep Learning. The ideal candidate will have hands-on experience developing, training, evaluating, and deploying machine learning and deep learning models to solve complex business problems.

Key Responsibilities

• Develop and implement end-to-end Machine Learning and Deep Learning models for real-world business problems.
• Perform data exploration, preprocessing, feature engineering, model selection, training, and evaluation.
• Apply statistical and mathematical techniques to identify patterns, trends, and insights from complex datasets.
• Build NLP solutions for text classification, sentiment analysis, entity recognition, information extraction, text similarity, and language understanding.
• Develop deep learning models using architectures such as CNNs, RNNs, LSTM/GRU, Transformers, and Attention mechanisms.
• Work with structured and unstructured data and develop scalable data science solutions.
• Perform model optimization, hyperparameter tuning, cross-validation, and error analysis.
• Evaluate models using appropriate metrics and establish model performance benchmarks.
• Collaborate with Data Engineers, ML Engineers, Software Engineers, Product Managers, and business stakeholders.
• Translate business requirements into analytical and machine learning solutions.
• Conduct experimentation and proof-of-concepts and convert successful approaches into production-ready solutions.
• Monitor model performance and continuously improve accuracy, scalability, and efficiency.
• Document methodologies, experiments, models, and results.

Mandatory Technical Skills

• Strong foundation in Core Data Science and Machine Learning.
• Strong hands-on expertise in Python.
• Experience with ML libraries such as Scikit-learn, Pandas, NumPy, SciPy.
• Strong understanding of supervised and unsupervised learning algorithms.
• Experience with:
  • Regression and Classification
  • Clustering
  • Decision Trees / Random Forest
  • Gradient Boosting / XGBoost / LightGBM
  • Feature Engineering and Selection
  • Model Evaluation and Validation
• Strong expertise in NLP concepts and techniques.
• Hands-on experience with NLP libraries/frameworks such as NLTK, spaCy, Hugging Face Transformers.
• Strong understanding of Deep Learning concepts.
• Hands-on experience with TensorFlow or PyTorch.
• Knowledge of neural network architectures including CNN, RNN, LSTM, GRU, Transformer, and Attention.
• Strong understanding of Statistics, Probability, Linear Algebra, and Optimization.
• Experience with SQL and working with large datasets.