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: 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.

📌 ML Intern (Delhi)
🏢 Sowiz
📍 Delhi