JP Morgan Data Science Interview
JP Morgan has assets of $2.6 trillion and is a leading global financial services firm.
JPMorgan Chase (NYSE: JPM) is one of the oldest financial institutions in the United States with a history of over 200 years. JP Morgan stock is a component of the Dow Jones Industrial Average. They have over 250,000 employees and they also have a presence in over 100 markets. All these stats about JP Morgan scream Data. With more than 150 petabytes of data, approximately 3.5 billion user accounts and 30,000 databases, JPMorgan Chase is definitely a name to reckon with in the financial sector. If you want to be a data scientist in the Fintech space, I cannot think of a company better than JP Morgan. DataQuery — Data and Analytics product is a multi-asset pre-trade analytics and data platform by JP Morgan which hires Data Scientists.
Interview Process
The interview process starts with a coding screen on collabedit. This is followed by an onsite. The onsite depends on the team. If there is a UX and analyst heavy team, that is where the onsite will go deep on. There are some technical teams doing hardcore ML and data science on one end of the spectrum and there are analyst heavy roles on the other end. The beauty of JP Morgan is if you lean heavily on either side as a Data Scientist, you can find a role with them. Occasionally, they do have take home challenges in some teams which is reflective of the culture of the team.
Important Reading
Apache Hadoop is the framework of choice for JPMorgan — not only to support the exponentially growing data size but more importantly for the fast processing of complex unstructured data.
- JP Morgan Github repo: JP Morgan Chase
- JP Morgan ML research: ML research blog
- NLP in Equity Investing: ML in Classification of News Sentiment for Equities
AI/Data Science Related Questions
- How would you explain the Linear regression to a non- technical person?
- What is the difference between Lasso and Ridge Regression?
- How do you explain map reduce programming to developer?
- How do you prevent overfitting?
- Explain Naive Bayes algorithm.
- Why do you need to apply feature scaling to logistic regression?
- Give an example where you applied linear regression.
- What methodology do you use to test your model?
- Find the intersection of two arrays of integers.
- Find the nth prime number.
- Use a binary search to find the index of a given value within an array of integers.
- How to correct for biases in your data?
Reflecting on the Questions
JP Morgan is making deep investments into Data Science by investing in its technology and people. Their interviews are well balanced between coding, ML and analysis questions. The Data Science team is very mature and hence, there is clear separation of duties between Data Engineers, Analysts and Scientists. The data scientist roles are specific to the vertical and the product, and the interview is a reflection of that. Good analytical and data sense as well as deep knowledge of their products can surely land you a job with one of the oldest financial institutions in the world.
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The sole motivation of this blog article is to learn about JP Morgan and its technologies helping people to get into it. All data is sourced from online public sources. I aim to make this a living document, so any updates and suggested changes can always be included. Please provide relevant feedback.
