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Anticipated Openings for Corporate - Intelligent Solutions & Finance, Data Science/Quantitative Methods - Associate

Req #: 180009370
Location: New York, NY, US
Job Category: Accounting/Finance/Audit/Risk
Potential Referral Amount: 3000 US Dollar (USD)

JPMorgan Chase & Co. (NYSE: JPM) is a leading global financial services firm with assets of $2.5 trillion and operations worldwide. The firm is a leader in investment banking, financial services for consumers and small business, commercial banking, financial transaction processing, and asset management. A component of the Dow Jones Industrial Average, JPMorgan Chase & Co. serves millions of consumers in the United States and many of the world's most prominent corporate, institutional and government clients under its J.P. Morgan and Chase brands. Information about JPMorgan Chase & Co. is available at

F3 Labs is a joint venture between JPMorgan Chase & Co's (JPMC) Global Finance & Business Management (GF&BM)  and Intelligent Solutions (JPMIS). Global Finance & Business Management (GF&BM)'s mission is to ensure the company is capable of navigating all types of market environments while maintaining a fortress balance sheet. Intelligent Solutions (IS) specializes in transforming JPMC's data assets into actionable analytics, insights and technologies that provide measurable business value for partners throughout JPMC.


The Lab’s work has historically revolved around the design and implementation of F3, JPMC's Firmwide Forecasting Framework – JPMC’s federated, end-to-end, multi-purpose forecasting platform.  In 2017, the Lab is shifting priorities to focus on incorporating advanced analytical tooling into F3.  Key lines of work include:

  • Automated analysis of forecast model output – which includes generation of automated benchmark forecasts, intelligent forecast comparisons, detection of potential anomalies of champion models with respect to benchmarks, and explanation of drivers of forecast misses and changes
  • Fast, interactive, assessment of the  impact of macroeconomic shocks on balance sheet, income statement, capital ratios, and other key metrics
  • Development of solutions  to easily understand the impact of changes in Firm’s strategy, originations, products under different economic scenarios through automated design of experiments
  • Extending  forecasting engines for Pricing, Valuation, and Optimization
  • Advanced ‘what if’ analyses and assessment of forecast uncertainty
  • Development of models for leading indicators to call cycle turns and plan through the Economic Cycle
  • Leveraging data assets for  knowledge discovery in data at large scale

We are searching for curious problem solvers to work in our next generation of forecasting and analytical tooling, with skills in one of the following areas, and willingness to learn a minimum of the other ones:

  • Statistics, Machine Learning and Data Science
  • Economics, Econometrics
  • Quantitative Development, System Architecture
  • Data Visualization, Analytical Applications, UI/UX

We offer an open, startup-like, dynamic and collaborative environment where you’ll have the opportunity to contribute initiatives that are transforming the industry, have ownership of entire workstreams and high visibility to senior management.


We work with every Line of Business (LOB), partnering with Finance and Risk teams to understand the business and user needs and collaboratively design solutions, and with Technology and Data Enginering to deploy solutions to production. Our partners include Retail Banking (Credit Cards, Mortgage, Auto Loans, Deposits), Commercial Banking, Investment Banking, Asset Management and Corporate Functions.

  • Excellent programming skills, preferably with experience in advanced R, Python
  • Knowledge of Machine Learning and some typical stacks for machine learning (e.g. scalable implementations of boosting, Tensorflow, MXNet, MLlib, scikit-learn, tooling in R's MachineLearning task view), and/or model computational statistics and typical frameworks (e.g. implementations of GAM's and GAMLSS methods in R, Stan). Stong understanding of the different methods – at the very minimum at the level of “intelligent user” of packaged methods, but ideally also knowing the algorithmic details at the level of being able to modify existing learning methods and even write new ones from scratch
  • Knowledge of typical big data tooling, preferrably with experience in Spark, Impala. Strong knowledge of data engineering – including databases (both relational and not relational), data I/O, cleansing, and transformation is highly desired, including avanced SQL analytical extensions for longitudinal data.
  • Knowledge of Econometrics (Time Series Analysis, Longitudinal Data Analysis) desirable
  • Polished communications and Data Visualization highly desirable.  Familiarity with patterns and typical libraries (e.g. D3.js, Material) and application frameworks (e.g. R’s Shiny, Angular, Polymer etc) commonly used to build highly interactive analytical applications
  • A balanced blend of pragmatism and solid theoretical grounds (yes, we do believe Ali Rahimi is mostly right, but at the same time, also understand that mankind have been building ships before hydrodynamics had been fully understood). Empirical rigor, and a deeply internalized sense of the scientific method, having zero tolerance for bad science or improperly substantiated claims.
  • An inquisitive mind with a healthy habit to dig deep in the questions to understand the context, ask hard questions, and reframe problems when needed
  • Strong analytical and problem solving, and more importantly, problem finding abilities
  • An advanced degree (preferably PhD or equivalent level of knowledge) in a quantitative field (e.g. computer science, finance, economics, electrical engineering, statistics, etc)

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