CIB Machine Learning Center of Excellence - Applied AI/ML Director
The Machine Learning Center of Excellence 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 perform groundbreaking work in machine learning methods, and tailor them to banking applications including risk assessment, trading models, customer relationship management, and pricing models. You will work broadly across the firm's lines of business to actively identify important business problems and formulate solutions. The machine learning techniques will include feed-forward, recurrent, recursive and convolutional neural networks, as well as maximum entropy models, and other models to be developed. Responsibilities may include recruiting, hiring, and team management.
- PhD in a quantitative discipline, e.g. Computer Science, Mathematics, Operations Research, Data Science, and 8 years of experience in a highly quantitative position.
- Experience in Deep Learning: DNN, CNN, RNN/LSTM, GAN or other auto encoder (AE).
- 5 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.)
- 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.