Applied Data Science (Fresher)

• Collect, clean, process, and analyze structured and unstructured data.
  
• Build and evaluate machine learning and statistical models.
  
• Perform exploratory data analysis (EDA) and identify meaningful patterns and insights.
  
• Develop data pipelines and prepare datasets for analytics and ML use cases.
  
• Work with engineering and product teams to solve data-driven problems.
  
• Evaluate model performance and continuously improve model accuracy and efficiency.
  
• Create visualizations and reports to communicate data-driven insights.
  
• Apply statistical techniques to support product and business decisions.
  
• Stay updated with current developments in machine learning, data science, and AI.
  
• Write clean, maintainable, and efficient code.
  

Requirements

• Strong proficiency in Python.
  
• Good understanding of Data Structures and Algorithms (DSA).
  
• Good understanding of Statistics and Probability.
  
• Knowledge of Machine Learning fundamentals.
  
• Experience with libraries such as Pandas, NumPy, Scikit-learn, and Matplotlib/Seaborn.
  
• Worked on LLMs tuning, replicated small/medium research papers, Dataset creation and some kind generated by their own.
  
• Understanding of data preprocessing, feature engineering, model evaluation, and validation.
  
• Basic knowledge of SQL and relational databases.
  
• Strong analytical and problem-solving skills.
  
• Ability to understand and work with large datasets.
  
• Good debugging and logical reasoning skills.
  

• Strong programming and DSA fundamentals.
  
• Curiosity to understand data and solve real-world problems.
  
• Strong mathematical and analytical thinking.
  
• Ability to learn quickly and work independently.
  
• Good communication and collaboration skills.
  
• Passion for building practical AI/ML solutions.