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 effective, vectorized code.
Modeling Expertise: Robust, 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: Solid grasp of linear algebra, multivariable calculus, probability, and optimization theory.

📌 Ml Intern Delhi
🏢 Sowiz
📍 Delhi