JP Morgan Chase & Co. (NYSE: JPM) is a leading global financial services firm with assets of $2 trillion and operations in more than 60 countries. The firm is a leader in investment banking, financial services for consumers, small business and commercial banking, financial transaction processing, asset management, and private equity.
The Machine Learning team at JPMorgan Chase combines cutting edge machine learning techniques with the company’s unique data assets to optimize all the business decisions we make. In this role, you will be part of our world-class machine learning team, and advance the state-of-the-art in financial applications ranging from pricing and credit models to natural language processing. Our work spans the company’s lines of business, with exceptional opportunities in each.
The successful candidate will apply sophisticated machine learning methods to banking applications including 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.
- 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 Chase's 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.
- 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.