Mar 31, 2022
We are seeking summer or fall internship/co-op candidates who are interested in projects related to time-series forecasting, predictive modeling, recommendation systems, and building generalized models from sparse data. The research outcome will directly impact the core technology we are building. Promising results will be accelerated to product deployment.
Implement generalized machine learning models that can work well with sparse data.
Implement associative learning through Graph Neural Network that can be combined with a predictive modeling approach for dynamic personalization.
Challenge state of the art methods for feature extraction from customer transaction data, data from marketing channels combined with the power of NLP that directly impacts building better recommendation systems..
Effectively communicate insights and recommendations to senior leadership and influence strategic decision-making.
Currently in the process of obtaining a Bachelor’s, Master's, or Ph.D. in a quantitative field (Applied Math, Engineering, Computer Science).
Strong fundamental knowledge in deep learning and classical machine learning algorithms.
Coding fluency in Python.
Experience with data science packages in Python. (Sckit-learn, pandas, PyTorch, NumPy).
Loves a fast-paced startup environment, thrives in chaos with a can-do attitude.
Prior experience in time series modeling, recommendation systems and clustering approaches is a plus.
The research publication track record is a plus.