Job Description

The Data Analytics team 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. In this role, you will be part of our industry-leading data analytics team, and advance the state-of-the-art in financial applications ranging from generating business intelligence to predictive models and automated decision making. Our work spans the company's lines of business, with exceptional opportunities in each.

 

Prerequisites

  • The successful candidate will apply data analytics techniques from traditional statistics to data engineering and some machine learning for banking applications.
  • Prior experience in finance in a must, either in Quantitative Research in Markets or Equity or Fixed Income Research.
  • Expected knowledge of machine learning techniques will include familiarity or knowledge of at least one of the following areas: time series analysis, supervised learning, pattern detection, natural language, maximum entropy models and neural networks.

 

Responsibilities

  • 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's lines of businesses and functions in the Corporate Investment Bank: Markets, Global Investment Banking, Corporate Banking, Technology and  Operations.
  • Lead your own project. Suggest, collect and synthesize requirements. Create an effective roadmap towards the deployment of a production-level machine learning application.



Qualifications

  • MS or PhD in a quantitative discipline, e.g. Computer Science, Mathematics, Statistics, 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).
  • 10 years of hands-on experience developing statistics models and 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.
  • Experience with big-data technologies such as Hadoop, Spark, SparkML, etc.
  • 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.)

 

Problem solving and collaboration skills

  • 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 independently research and propose alternatives with some guidance as to problem relevance.
  • Must be able to undertake basic and advances EDA, may require some direction from more senior team; should be aware of limitation and implication of methodology choices.
  • Ensures re-use and sharing of ideas within team and locale.
  • Able to work with non-specialists in a partnership model, conveys information clearly and creates a sense of trust with stakeholders.
  • Shows institutional awareness and some understanding of applied problem solving, may require coaching and guidance as to how to most rapidly reach a satisfactory conclusion
  • Must be able to train others in these areas, and is seen as a leader within the broader DS community; taking leadership of topics and areas in order to help others grow
  • Seeks to publish work internally, and ideally externally, which would also include speaking / teaching at appropriate venues (either internal or external)
  • Is able to envisage the end to end solution for a problem, including data, technology and methodological decision; either alone in conjunction with technology partners.
  • Takes the lead in sharing best practice within the CIB analytics team
  • Takes a structured, incremental approach to delivery, inclusive of stakeholders, with clear transparency of plan, and consistent track record of delivery.
  • Is able to weigh up conflicting priorities in delivering a solution, and can rapidly evaluate the impact of tradeoffs.  Further is able to convey this in language that is audience appropriate, and qual / quant describe the impact of such  decisions
  • Has a strong technology understanding and can assess the most appropriate solution options throughout the lifecycle (in conjunction with tech partners); remains current on industry developments, and is able to evaluate their impact and value to the team and organization.  Will take decisions that are optimal for the firm even when locally suboptimal.
  • Manages multiple interdependent project, ensuring transparency, and management of critical path risk;  champions delivery approach and looks for incremental process improvements.

Application Instructions

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