DescriptionData Analytics at JPMorgan Corporate Investment Bank combines cutting edge machine learning techniques with the company’s unique data assets to optimize all the business decisions we make.
As an AI Engineering Director within JPMorgan Corporate Investment Bank, you will promote key strategic Artificial Intelligence and Machine Learning programs and transformation initiatives aimed at enhancing business processes for Corporate Investment Bank Operations. You role will involve delivering AI/ML infused products using cloud infrastructures, managing data from large databases/data-sources, and collaborating with various teams across the firm. You will have superior technical abilities and an ability to recommend, influence and implement change.
Job responsibilities
- Able to deliver production-level AI/ML microservices
- Wrangle data from large databases/data-sources and expose them as reusable data products
- Communicate final results and give relevant business context
- Adjudge and explain what value certain software engineering models and practices bring to current line of work
- Collaborate with other J.P. Morgan AI, machine learning and quantitative teams
- Work closely with ML/AI, technology, product, and business partners across the firm to maximize sharing of information and best-practices
Required qualifications, capabilities, and skills
- PhD in a quantitative discipline such as Computer Science, Mathematics, Statistics, Operations Research, Data Science
- Hands-on experience in implementing distributed/multi-threaded/scalable applications (including frameworks such as Horovod, Ray, DeepSpeed, etc.)
- Prior experience with data-engineering aspect (ETL operations on large database/storage systems) of ML software design and with big-data technologies such as Hadoop, Spark, SparkML, or similar
- Excellent Python and SQL programming skills and familiarity with standard data science tooling and understanding of algorithms and software engineering fundamentals
- Well-versed with deep learning libraries such as PyTorch or Tensorflow
- Proven track record of technical task leadership and effective people management, including attracting and developing top talent
- Deep understanding of machine learning techniques in several of the following areas: time series analysis, (un)supervised learning, deep learning, knowledge graphs, natural language processing, regression analysis and maximum entropy models