Data 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. We are looking for an outstanding, Engineering Director. A highly motivated, self-starter with strong analytical skills and thought leadership is required to drive key strategic Artificial Intelligence and Machine Learning programs and transformation initiatives designed to enhance business processes for Corporate Investment Bank Operations.

As an Engineering Director, you must have a proven track record of superior problem-solving skills through previous experience in delivering AI/ML infused products using cloud infrastructures (AWS, Azure or GCP), program management, financial controls, business/technology, data management, senior management presentation creation and influencing stakeholders. The role demands superior analytical skills, technical abilities, flexibility to manage multiple deliverables, an ability to recommend, influence and implement change, a confident leader possessing a strength of personality and intellect to gain the respect of demanding business heads and senior partners.

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