ML Intern

Job highlights

• Recent graduate with advanced Python skills and expertise in tree-based models (CatBoost, XGBoost, LightGBM) and neural networks, plus strong knowledge of SQL and NoSQL databases
• Develop, train, and optimize machine learning models for forecasting and decision support, manage large-scale data pipelines, and conduct rigorous model evaluation and experimentation

Role & responsibilities

• Model Development & Iteration: Develop, train, and rigorously iterate on machine learning models tailored for forecasting and decision-support use cases.
• Modeling Expertise: Architect and optimize robust models utilizing strong expertise in tree-based models (CatBoost, XGBoost, LightGBM) alongside deep learning frameworks (PyTorch/TensorFlow).
• Data Engineering & Storage: Extract, transform, and manage large-scale datasets leveraging both SQL and NoSQL databases to build high-throughput feature engineering pipelines.
• Optimization & Deployment: Apply hyperparameter tuning, model compression, and optimization techniques to maximize inference efficiency.
• Evaluation & Experimentation: Design rigorous validation strategies, analyze error distributions, and track experiments using tools like MLflow or Weights & Biases.

Preferred candidate profile

• Education: Recent Graduate (holding a B.Tech, M.Tech, MS) in Computer Science, Statistics, Mathematics, Electrical Engineering, or a closely related quantitative field.
• Programming Proficiency (Required): Advanced, production-level Python programming experience is strictly required, accompanied by deep familiarity with NumPy, Pandas, and writing efficient, vectorized code.
• Modeling Expertise: Strong, hands-on understanding and implementation experience with tree-based models (CatBoost, XGBoost, LightGBM) as well as neural networks.
• Data Systems: Solid working knowledge of querying and managing data across both SQL and NoSQL databases.
• Core Math Foundation: Strong grasp of linear algebra, multivariable calculus, probability, and optimization theory.