Data Science Internship (Full-time) - Zippia - San Francisco

Zippia, Inc. is an early-stage consumer internet start-up in Silicon Valley. Our mission is to
empower people with the best information and tools necessary to achieve their career
aspirations. Our co-founders have worked together before and have started multiple technology
and internet businesses previously and had multiple successful exits.

We are looking for a Data Science Intern(s) with keen interests in natural language processing
and machine learning to be part of our Data Science team. The ideal candidate is a recent
undergraduate who is interested in acquiring experience in a fast moving and rich learning
environment while helping us build data-driven products to help job seekers fulfill their goals.
You will work with a wide range of large unstructured and structured datasets and design
methods to extract new useful information from them, as well as implement improvements on
top of existing methods.

Due to the training time necessary to achieve familiarity with our existing infrastructure and
datasets, we have a strong preference for candidates that are able to commit at least 1 year of
their time to this position.

Key Qualifications :
- Familiarity with supervised and unsupervised machine learning algorithms for regression,
classification, and clustering. Familiarity with information retrieval algorithms is a plus.
- Familiar with classical statistics and time series analysis.
- Familiar with Python, proficiency is a plus.
- Experience with natural language processing and related libraries.
- Agile, action-oriented, quick thinker: you can deliver preliminary results fast by simplifying
assumptions, and take time to dive deeper if the problem demands more scientific rigor.
- Experience using Git on a team setting.
- Undergraduate in one of the following: Computer Science or Engineering (with focus on
artificial intelligence, data mining, or machine learning).
- Preferred: Experience in NoSQL (MongoDB + Elastiseaerch)
- Excellent communication skills, both written and spoken, in English.

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