Purpose

The Data Scientist should have a graduate degree in Computer Science, Statistics, Deep Learning or Machine Learning and a strong background in statistical methods such as Bayesian statistics, time series forecasting, and feature engineering. Candidates with an undergraduate degree and a demonstrable track record in these domains are encouraged to apply.

Responsibilities

1. Implement state-of-the-art deep learning and machine learning models
2. Conduct original research on our large repository of data, both proprietary and open-source
3. Write production-level code linking new and existing data pipelines with scripts for feature engineering, machine learning predictive models and visualization
4. Write tests to check for integrity of our data, models and predictions

Requirements

1. Comfortable with core machine learning algorithms implementation and theory
2. Advanced in scripting languages (Python, R and UNIX Shell), Git project management, deep learning frameworks (PyTorch /Keras / TensorFlow..), and programming skills (Java or C++)
3. Can communicate clearly and cogently on concepts, processes and algorithms used
4. Ability to work in time-sensitive environments and to approach problems from different angles
5. Deep learning experience

Differentiating Factors

1. A vibrant Github account with open-source repositories useful to the community
2. Experience at a corporate data science team, financial/investment analytics entity, or hedge fund
3. Work with alternative data (e.g. news, social feeds, etc.)
4. A top ranking in online AI competition platforms such as Kaggle