Computer Science Jobs

There are a broad range of computer science jobs; with the right classes, internships, and independent projects, Yale graduates have the kind of preparation necessary to pursue them all. Below we highlight the top five functional areas that Yalie’s interested in computer science tend to pursue after they graduate:


These positions range from developers (back-end coders) to designers (front-end web designers) to product managers (guide the success of a product and lead the cross-functional team that is responsible for improving it). Yale students pursue this functional role in a wide-range of industries, from larger tech firms with strong software engineers to small start-ups where you’d get exposed to exciting new ideas to medium-sized pre-IPO organizations, which are a combination of the two.


There are a wide range of start-ups, including those that focus on software, hardware, and biotech. While some computer science and mechanical engineering students pursue hardware work, most Yale graduates focus on the software embedded in the hardware system (this software is different from web/app development). This could include innovative work on things like smart homes, smartphones, and smart speakers. Yale biomedical engineering students receive strong training for a range of biotech start-up positions, including how to solve clinical dilemmas using computational modeling. When pursuing start-ups, which requires a lot of individual outreach and cold emailing, consider what stage the start-up is in (e.g. early, middle, growth), who is funding them, and how successful the funder’s initiatives have been in the past.

Data Science & Statistics

There are a wide range of definitions for what a “data scientist” does. Generally speaking, this is a new term for a collection of things that already exist: data modeling, data mining, statistical analysis, predictive analytics, machine learning, etc. A data scientist role usually requires three primary skillsets: 1) An understanding of advanced statistics; 2) An ability to program and use a variety of analytical tools; 3) The ability to grasp business/domain concepts so you can ask the right questions and understand how to interpret the results in context. Data scientists understand a range of big data programming languages (e.g. R, Python, MATLAB, Scala) and are increasingly doing work in machine learning, which is a field of study that gives computers the capability to learn and to become more accurate in predicting outcomes without being explicitly programmed. They also explore areas of deep learning, which is an aspect of artificial intelligence that is concerned with emulating the learning approach that human beings use to gain certain types of knowledge.

Quantitative Finance

Quantitative analysts use mathematical models and extremely large datasets to analyze financial markets and securities. They help firms make better-informed financial and business decisions when it comes to pricing, investment and so on.


Computer science research, which is primarily conducted by PhD’s, moves much faster than any other research – you can publish 6-12 papers in one year. While you can pursue CS research at a university, you can also pursue it with a corporate research group and study things like AI, self-driving cars, and Visual and Augmented Reality.

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