Python Predictive Analysis Intern

Key Responsibilities

• Analyze large and diverse datasets to identify patterns, trends, and insights using Python and analytical libraries.
  
• Prepare, clean, preprocess, and validate data for predictive modeling, NLP, and machine learning tasks.
  
• Design, build, train, and evaluate predictive, classification, clustering, and time-series models.
  
• Assist in the development, training, fine-tuning, and evaluation of Large Language Models (LLMs) and NLP models using Python and relevant ML frameworks.
  
• Implement machine learning and NLP algorithms using clean, efficient, and well-documented Python code.
  
• Visualize complex data and model outputs, and communicate actionable insights to technical and non-technical stakeholders.
  
• Collaborate with business users, data scientists, and engineers to translate objectives into analytical and AI-driven solutions.
  
• Participate in model evaluation, performance optimization, and error analysis to improve accuracy, robustness, and scalability.
  
• Support automation of data pipelines, model training, inference, and deployment workflows.
  
• Document data sources, modeling approaches, experiments, and results to ensure reproducibility and transparency.
  
• Stay updated with the latest advancements in predictive analytics, machine learning, NLP, and LLM technologies.
  
• Actively contribute to team discussions, code reviews, and knowledge-sharing sessions.
  

Required Skills & Qualifications

• Bachelors degree (currently pursuing or recently completed) in

  Computer Science, Data Science, Artificial Intelligence, Machine Learning, Statistics, Mathematics, or a related field

  .
• Strong proficiency in

  Python

  , with hands-on experience in libraries such as

  Pandas, NumPy, scikit-learn

  , and data visualization tools.
• Understanding of

  predictive analytics, statistical analysis, machine learning concepts, and data modeling techniques

  .
• Basic knowledge of

  NLP concepts and Large Language Models (LLMs)

  .
• Familiarity with deep learning frameworks such as

  PyTorch or TensorFlow

  is an advantage.
• Exposure to working with

  large datasets

  , text data preprocessing, and feature engineering techniques.
• Experience or familiarity with tools/libraries such as

  YOLO, MediaPipe

  , or similar ML frameworks is a plus.
• Strong analytical, problem-solving, and logical thinking skills.
  
• Good communication skills with the ability to explain technical concepts clearly.
  
• Ability to work collaboratively in a team environment and demonstrate eagerness to learn and adapt to new AI technologies.