Before Applying

I would say that almost everything from the

software engineering page’s preparation section⁠

applies, with some special points of emphasis for data science positions.

For DS positions, your publications will be very important. Be sure to link any published papers you have in the “Publications” section of your Linkedin (a brief summary of the scale of data, DS methods you applied, and results would be excellent). For papers that are unpublished, see if you can put them on ArXiV (check if the journals/conferences your papers have been submitted to allow pre-prints) and put these on your LinkedIn

List any courses you’ve taken or skills you have related to data science in your courses/skills sections. Data scientists must be quite versatile so showing that you have a broad data skillset is important (everything from data viz to data engineering to dashboarding to machine learning/deep learning to computer vision)

Experience

Research (specifically published research) is critical for landing DS/ML positions. While engineering tends to generally look for “engineer”-y type people, DS/ML roles are typically looking for more “research”-y type people (the nature of the job requires a lot more experimentation in building predictive models & good data instincts in diving down rabbit holes to try to extract insight from messy datasets). Try to involve yourself in research positions where you are working with data and applying data science techniques (which will ideally, lead you to publications)

Getting these positions: the good news is data science is everywhere and is needed across so many fields. Geologists need to analyze data to determine the age of fossils, economists use regression to project various economic factors, etc. Talk to your professors, look at the

undergraduate research database⁠

, etc. and I assure you you will be able to find a DS research project.

Independent research: even if you are unable to land an official research position, you can try to do research on your own time. Take any dataset, apply some novel methods on it, and you may be on track for a publication (e.g. one of my friends analyzed data on pregnancy-related deaths, submitted it to surgical oncology, and it got published). However, I would still advise against independent research since you can learn more by working with a MS/PHD student and you’ll more likely get published if you have the backing of an official lab.

Note: independent research can also be thought of as personal projects.

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