Machine Learning Developer

Austin, TX 78707

Posted: 08/13/2018 Category: Quant Job Number: 10739

Machine Learning Developer


This position is most suited to individuals who are looking to pursue a career in Fixed Income, Currencies and Commodities research and Portfolio Management working directly alongside Research analysts, portfolio managers and traders.

The candidate will develop new machine learning methods tailored to banking applications such as risk assessment, trading models, customer relationship management, and pricing models. Machine learning techniques will include feed-forward, recurrent, recursive and convolutional neural networks, maximum entropy models, and other algorithms related to time series analysis and supervised learning.

  In addition to using and adapting existing models, you will be expected to develop new techniques especially suited to banking applications. The candidate will help in the identification and solution of new problem areas.

  • Develop scalable tools leveraging machine learning and deep learning models to solve real-world problems in areas such as Time Series predictions.
  • Collaborate with Analytics and Portfolio Management
  • Create an effective roadmap towards the deployment of a production-level machine learning application.
  • 4+ years of hands-on experience developing machine learning models.
  • Ability to develop and debug in Python, and C++.
  • Extensive experience with machine learning APIs and computational packages (TensorFlow, PyTorch, Keras, Scikit-Learn, NumPy, SciPy, Pandas, statsmodels).
  • Familiarity with basic data table operations (SQL, Hive, etc.)
  • Should be able to work both individually and collaboratively in teams, in order to achieve project goals.
  • Must have the ability to design or evaluate intrinsic and extrinsic metrics of your models performance which are aligned with business goals.
  • Must be able to effectively communicate technical concepts and results to both technical and business audiences.


Beneficial Skills
  • MS or PhD in a quantitative discipline, e.g. Computer Science, Mathematics, Operations Researc h, Data Science, or similar BS with 2+ years of experience in a highly quantitative position.
  • Solid time series analysis, speech recognition, NLP or financial engineering background.
  • Strong background in Mathematics and Statistics.
  • Published research in areas of Machine Learning, Deep Learning or Reinforcement Learning at a major conference or journal.
  • Experience with GPUs and cloud-based training of deep neural networks.
  • Experience with big data technologies such as Hadoop, Spark, SparkML
Machine Learning, Python, Computational Packages

Evan Pollock

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