Machine Learning Engineer - Vice President

  • New York, NY

Compensation

: $145,793.33 - $49,680.00 /year *

Employment Type

: Full-Time

Industry

: Manufacturing



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Develop scalable tools leveraging machine learning and deep learning models to solve real-world problems in areas such as Speech Recognition, Natural Language Processing and Time Series predictions.
Collaborate with all of JPMorgan Chases lines of businesses, such as Investment Bank, Commercial Bank, and Asset Management.
Lead your own project. Suggest, collect and synthesize requirements. Create an effective roadmap towards the deployment of a production-level machine learning application.
MS or PhD in a quantitative discipline, e.g. Computer Science, Mathematics, Operations Research, Data Science, or similar BS with 2+ years of experience in a highly quantitative position.
Experience in Deep Learning: DNN, CNN, RNN/LSTM, GAN or other auto encoder (AE).
2+ years of hands-on experience developing machine learning models.
Ability to develop and debug in Python, Java, C or C++. Proficient in git version control. R and Matlab are also relevant.
Extensive experience with machine learning APIs and computational packages (TensorFlow, Theano, 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 be curious, hardworking and detail-oriented, and motivated by complex analytical problems.
Must have the ability to design or evaluate intrinsic and extrinsic metrics of your model s 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
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.
Contribution to open-source projects on Machine Learning.
Knowledge in Reinforcement Learning or Meta Learning.
Experience with big data technologies such as Hadoop, Spark, SparkML, etc


Associated topics: chief executive, chief executive officer, chief information officer, chief technology officer, co founder, director, president, senior director, vice president

* The salary listed in the header is an estimate based on salary data for similar jobs in the same area. Salary or compensation data found in the job description is accurate.

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