IT Data Scientist (Machine Comprehension) for an AI and Machine Learning Company

Purpose

We are looking for exceptional Machine Comprehension Data Scientists to embark on our journey to deploy world class deep learning and machine learning algorithms in the investment and portfolio management space.

The Data Scientist (Machine Comprehension) should have a PhD or Masters in Computer Science, Statistics, Deep Learning or Machine Learning and a strong background in statistical methods such as Bayesian statistics, time series, and feature engineering.

If you have a passion for building state-of-the-art deep learning and machine learning models and have a keen interest in Natural Language Processing and semantic AI, this is your opportunity.


Responsibilities

  1. Implement state-of-the-art deep learning and machine learning models for feature engineering and question/answer/prediction in investment platform
  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. Question and Answering modelling (Squad/ CNN News dataset)
  4. Can communicate clearly and cogently on concepts, processes and algorithms used
  5. Ability to work in time-sensitive environments and to approach problems from different angles
  6. Deep learning experience
  7. Knowledge of basic finance will be a plus


Differentiating Factors

  1. Published at top deep learning conferences like ICLR, NIPS and ICML.
  2. Participated in SQUAD competition and got good F1 score above 80% or Github machine learning project.
  3. A vibrant Github account with open-source repositories useful to the community
  4. Significant experience at a top research lab, financial/investment analytics entity, or hedge fund