- Apache Hadoop
- Big Data
- Data Management
- Data Science
- Equity Derivatives
- Inventory Management
- Machine Learning
- Microsoft Access
- Operational Risk
- Risk Management
CIB QR – Quantitative Research – Equity Derivatives Analytics – Associate / Vice President
Understand the business processes and their data
Industrialize availability of data
Develop data-driven decision making analytics through reports, analytics, predictions and optimization
Implement in a production setting
Provide strong control environment around data access, model risk and operational riskThe team is expected to cooperate closely with the QR Equities Modeling and Technology teams and the other QR and Data functions to ensure leverage across the various data science efforts in JPMorgan.
Signal-driven idea generation
Data-driven trading decision making
Automatization of derivatives risk management, pricing and inventory optimization
Perform large-scale analysis on our proprietary datasets
Identify new insights that drive feature modelling
Build prototype models with data pipelines that serve as roadmap to production
Make real-world, commercial recommendations through effective presentations to various stakeholders
Leverages data visualization to communicate data insights and results
The ideal candidate brings quantitative experience in the Equity business (products, models, market standards and business practices), combined with a background in machine learning techniques / statistics, and a curiosity to expand in this field. . Communication skills and drive are critical for the role as we expect the candidate not only to help defining future business practices but also to bring cultural change towards a modern data-driven approach to business.
A master’s or Ph.D. degree program in computer science, statistics, operations research or other quantitative fields
Strong technical skills in data manipulation, extraction and analysis
Fundamental understanding of statistics, optimization and machine learning methodologies
Mastery of software design principles and development skills using one of C++, Python, R, Java, Scala.
Confident in technology in particular around data management. Knowledge in KDB and Big Data solutions such as Hadoop/Spark, Hive etc advantageous
Previous practical experience in solving machine learning problems using open-source packages (such as sklearn). Experience in TensorFlow or other RL packages is advantageous.
Participation in KDD/Kaggle competition or contribution to GitHub highly desirable
Strong communication skills (both verbal and written) and the ability to present findings to a non-technical audience