Senior Machine Learning Developer

1. Experienced with Python, able to develop production system (object-oriented programming, unit tests, etc.)

Specific libraries / experience, these are not required if they have a lot of experience in Python they could easily learn the tools.

- Numpy, Scipy, and Pandas, used for Data Science and data-cleaning
- Scitkit-learn, familiar with Machine Learning libraries and pipeline
- Keras and/or Tensorflow, Keras will be used for R&D, Tensorflow for production systems
- Some form of plotting / visualization tools, Matplotlib, Seaborn, etc.
- Unittest / Pytest, used for testing code
- Familiar with building mid to large sized Python projects, with multiple modules, packages, etc.

Bonus would be any of the following.

- Able to demonstrate experience building a Python software project, rather than just simple scripts.
- Example or experience building Machine Learning systems that are used in the real-world, rather than just simple Kaggle contests.

2. Experience with Machine Learning and Data Science

Previous work experience in a Machine Learning and Data Science role, where they have explicitly demonstrated work experience in these areas.

- Working with real data, where they had to process large amounts of data, clean it (resolve errors), and where they demonstrated the knowledge of how to intelligently correct/clean erroneous data (imputation, Kalman filter, etc.)

- Experience with Jupyter Notebooks or other data science workbook/visualization tools used to perform R&D and assess/analyze data and test new models and hypotheses.

- Create Machine Learning models for real-world problems, such as classification (classifying data), or regression (numerical values such as revenue/earnings forecasts).

- Experience with traditional Machine Learning models is fine and was the norm for many years, Support Vector Machines (SVM), Logistic Regression, Random Forests, Naive Bayes, Ensemble methods, etc.

- Experience with Deep Learning systems, in particular Deep Learning systems that are used to learn and predict patterns in abstract sequences of data such as RNNs, LSTM, GRUs.

- Experience with tools/libraries used in Machine Learning, specifically Scikit-learn, Tensorflow and/or Keras

Bonus would be any of the following.

- Experience building Machine Learning models and/or Deep Learning on time-series data

- Experience with Tensorflow and/or Keras building RNNs, LSTM, or GRUs.

- Built or worked with multi-GPU (clustered) systems in Tensorflow

- Transfer learning and / or Reinforcement learning, anyone with above bonus skills would likely be able to pick up these if they don't know them.

3. Work Commitments

The role is available for remote work, however the following will be required as part of project management.

- Must be available at least 1-2x a week for meetings with project manager and other ML team members

- Available on Slack to discuss with other ML team members and troubleshoot problems

- Work with Wrike project management system used to assign tasks, no prior experience with Wrike is required.

- Familiarity with using git, able to commit work to main repository shared with other ML team members.

Desired Skills

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Posted: Jan. 11, 2018

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