LSESU Data Science Society: Tech Internship Panel

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A wonderful panel of 6 LSE students who have partaken on tech/data science internships will be sharing their experiences interning at a range of exciting firms including Barclays, Bank of England and G-Research!

Thanks to : Aaron Khoo, Derek Wang, Jeria Kua, Maja Lie, Mun Fai Chan Wian Stipp.

Profiles

Aaron Khoo

Derek Wang

Jeria Kua

Maja Lie

Mun Fai Chan

Wian Stipp

Recap:

Biggest takeaway / Skills gained from internships

  • Wian: From "Lanterne", how to build full end-to-end software - migrating/deploying to AWS.

  • Mun Fai: Internship at Singapore Department of Statistics had research focus as opposed to modelling - machine learning. Data Science is not just about machine learning models.

  • Maja: Soft skills - interacting with stakeholders, getting the right data from the people are critically important.

  • Jeria: It's not just about hypothesis testing and modelling - summarising statistics, data viusalisations - is important, data needs to be communicated professionally to the public

  • Derek: Learning how to collaborate on teams on a more technical internship vs just working on own

Technical Skills & Application Process

  • Aaron: Coding tests - Leetcode, Object Oriented Programming. CV, Online Tests (Situational Judgement Test). 3rd stage was programming test.

  • Derek: General application, psychometric tests (numerical ,logical), video interviews. Assessment Centre

  • Jeria: Cultural fit, interview with statisticians. Python (numpy, pandas), R, Excel (VLOOKUP, Macros helpful) are helfpul to know.

  • Maja: Numerical reasoning test (stats, logic, interpreting graphs), face-to-face interview

  • Mun Fai : General application. Choose one of Python & R. Internship involved looking at niche packages (100s of users on Github) - so being able to read documentation and source code - to understand how these niche packages work is useful.

  • Wian: Know one language - recommends Python - as it ahs libraries for statistical models, but also teaches general software engineering. Also strive for a good mathematical / statistical background.

Top Tip

  • Aaron: Don't be afraid to apply! The tech industry isn't as scary as you might think! Be sure to ask others for help!

  • Derek: Apply as early as you can; the later you apply the more people you compete with. Make sure to have your skills clearly shown in your CV! (Initially rejected by Bank of England but called again due to DS experience on CV)

  • Jeria: Be open minded about the industries you consider - e.g. governmental departments. There are tech opportunities everywhere!

  • Maja: Be willing to learn and well rounded and dynamic! Read the News! Also Women please apply! It's not just a men's field. Dont be afraid to apply - just go for it!

  • Mun Fai: Take as many maths, stats, econometrics courses as you can - especially if you want to break into data science - vs a CS student your programming skills would not be as strong, but you can excel in understanding the mathematical side.

  • Wian: Specialise in a particular area - Natural Language Processing, Computer Vision, Time Series. Be creative, don't just fit a model. Do more than what is expected.

07/10/2020