Wharton Research Data Services (University of Pennsylvania) | Data Scientist | Philadelphia, PA | ONSITE | VISA
We are looking for a full-time Data Scientist to join us at Wharton Research Data Services (WRDS), a Wharton department that provides business intelligence, data analytics, and research support to academic institutions around the world.
## Technical Details
·  Programming languages: Python! You will make use of the `pandas` module extensively in daily work. Knowing how to parallelize your computation is a bonus (we work on a 40-core server). It would help to explain your work to your fellow colleagues if you know SAS, but it is not a required skill.
·  Machine learning: "Shallow" learning techniques (such as SVM classifiers with `scikit-learn`) would help a lot, while neural network packages (such as Tensorflow/Keras) would be an overkill.
·  Environment: All our R&amp;Ds are performed on a Linux server. You need to be comfortable with terminal access, Linux commands, SSH tunneling, and package management with `conda`. Our filesystem is NFS, so knowledge of optimizing IO for cache utilization would be great.
·  Delivery: You need to be good at clearly summarizing your work by writing reports and documentations. By "clear", we mean "easily understandable by financial analysts who have little CS background". In terms of format, Word is good, but LaTeX or Markdown would be a delight. You will also need to pack your derived datasets and/or codes in a portable way, so that our data team can add them to our client-facing database. You can choose your own version control solution.
·  Computational resources available: We have Jupyter Lab deployed on our internal R&amp;D server, as well as a huge SAS cluster shared with our clients. Most of our numerical data are on PostgreSQL. Feel free to set up our own MongoDB, etc., if needed.
## Preferred Background Knowledge in Finance
We are in the School of Finance, after all. While not required, these background and experience will be preferred:
·  experience with business/financial/accounting analytics based on large datasets,
·  knowledge in finance,
·  good at foosball,
·  experience working with SEC data (including textual filings and numerical data), and
·  experience with financial databases (e.g. CUSIP, CRSP, and Compustat).
## Apply
If you are interested, please apply through Penn Human Resources at <a href=" .
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