JPMorgan Chase & Co. (NYSE: JPM) is a leading global financial services firm with assets of $2 trillion and operations in more than 60 countries. The firm is a leader in investment banking, financial services for consumers, small business and commercial banking, financial transaction processing, asset management, and private equity.
JPMorgan Intelligent Solutions (JPMIS) is the group considering ways to transform and leverage JPM proprietary data assets into opportunities for JPM. Protecting and managing intellectual property effectively as well as utilizing it to develop solutions that are both customized and scalable will enable JPM to create additional shareholder value.
The Intelligent Solutions, London team work on projects that deliver real business value in short time frames. All team members will have a core competency in software development and/or data science and have specialist experience in delivering real data science solutions.
Specifically, the JPMIS Data Scientist is responsible for developing, testing and evaluating quantitative models in a consulting-style environment, which places a premium on problem solving and client interaction. The specific role is dedicated to projects sourced from Markets, Investor Services and Investment Banking, Asset Management and/or Wealth Management so knowledge of wholesale business and economics is key. Candidates will be measured by their hands on experience with applying machine learning techniques to real life problems first, and quantitative finance knowledge, second.
Participate in discussion of product design and usage, including engagement with end users
Develop, expand and evaluate machine learning models
Develop production quality code using Hadoop/spark stack.
Participate in discussion of data modelling and algorithmic design
Strong statistics/mathematics skills
Strong written and oral presentation / communication skills
industry experience required
Candidate must have prior experience with
Clustering techniques such as k-means, decision trees or random forests
Regression techniques, both linear and nonlinear
Machine learning techniques including supervised and unsupervised models
Financial markets with experience in developing financial risk/pricing models (multi-asset experience a plus)
Proficiency with the following is preferred
Scientific/numeric software such as Python/NumPy/SciPy
Data frame software such as Pandas
Time series analysis including ARIMA models
Distributed programming models such as MapReduce using Big Data technologies such as Hadoop or Spark
Ability to interact and influence both internal & external business partners
Desire to learn, desire to grow expertise in quantitative modeling and work within a team
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