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Senior Data Scientist VP/ Associate- Intelligent Solutions

Req #: 170081774
Location: New York, NY, US
Job Category: Technology
Job Description:
JP Morgan 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.
JP Morgan Intelligent Solutions (JPMIS) is a new 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 JPMIS Senior 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 all internal lines of business, with a focus on Credit and Risk.
Primary Responsibilities
  • Develop, expand and evaluate demographic models, including cluster modeling and distance metrics
  • Develop, expand and evaluate recommendation engines for securities and financial products
  • Partner with JPM quants, technologists and business leaders to model, price, and implement trading and/or risk management strategies across all asset class
  • Lead discussions in data modeling and algorithmic design
  • Participate in discussion of product design and usage, including engagement with end users
  • Education/Skills Requirements
  • Ph.D. or Masters in STEM field
  • Strong Java/C/C++ and SQL coding skills
  • Strong statistics/mathematics skills
  • Strong written and oral presentation / communication / visualization skills
  • 5-10 years industry experience
  • 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)
  • Proven delivery record for quantitative models and projects
  • Proficiency with the following is preferred
  • Statistical software such as R or SAS
  • Scientific/numeric software such as MatLab or 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 and external business partners
  • Desire to learn, desire to grow expertise in quantitative modeling and work within a team
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