Careers in Data Science and Technology for LSE Students

The following section is a collection of resources about applying to, or careers in Data Science and more broadly, careers in technology.

Disclaimer: This is informal advice and purely opinion, read at your own caution

We have a members groupchat: dsatlse.zulipchat.com where we share more careers opportunities / events

Careers in Data Science and Technology for LSE Students

There are numerous careers in Data Science and Technology / Software Engineering- it depends broadly on your (career) interests.

Despite being the Data Science society, why include Software Engineering / Technology? The reason is that many companies do not offer data science roles / internships for undergraduates - but the Software Engineering or "Technology" route could serve as an indirect avenue to doing data science, and in addition they can develop your programming and problem solving skills (which are very useful).

In addition, you may be interested in data science and technology - but not be extremely into the technical / programming aspects - for that, "technology" encompasses many other roles, such as business analyst, product manager and so on.

Roles in Data Science:

Data Scientist

Machine Learning Engineer

Data Engineer - Involved in building sytems - https://github.com/datastacktv/data-engineer-roadmap

But many other roles utilise data science - academics, traders, consultants.

1st years

Spring Weeks

Many banks and several other companies offer "Spring Weeks" and other "Insight programmes" in Technology, a 1 week insight into how technology is being used and what a technology role in the company would involve. An advantage of applying to a Spring Week, is that some of them will convert successful participants to the summer internship for your 2nd year. A non-exhaustive list (might not be updated / accurate):

Banks: Bank of America, Barclays, Citi, Credit Suisse, Deutsche Bank, HSBC, JP Morgan, Morgan Stanley, UBS, Nomura, Macquarie, RBC, RBS
Asset Managers: Blackrock, Schroders, Point72
Trading: Flow Traders, Optiver, Citadel
Advisory / Concultancy: EY, KPMG, Deloitte, PWC, McKinsey
Tech: Bloomberg

Internships

There are not that many internships at big companies available for first year internships - they want to be able to have you on as a graduate the year following the internship - you may need to find smaller companies, or network. A non-exhaustive list:

Metaswitch Networks
Bank of England first year internship
Google Summer of Code (GSoC)
Google STEP

2nd years

Internships

Look for a "Data Science" internship in your second year, or otherwise a "Software Engineering" / technology internship. Since many LSE students are interested in banking and finance (and you are also interested in technology), a technology role in a bank may be the way to go - LSE students may have an advantage in that in addition to knowing some programming and data science (because of the society), students have direct knowledge of finance but also the social sciences, which could offer an unique perspective.

3rd years

Internships

This depends on if you want to pursue a masters, if so then apply to the internships again. Otherwise look for graduate opportunities.

Masters

Look for a Data Science / Machine Learning / Artificial Intelligence masters. These will likely require a strong mathematical foundation (Multivarite Calculus / Linear Algebra / Statistics - MA212, ST202). There are also Computer Science conversion masters for students who studied different subjects in undergrad (including Econ, social sciences).

Phd

If you really enjoy learning more about theory and doing research, you might be intersted in doing a PHD

Applying to a PhD

Working on a PhD

ML Academia: https://bastian.rieck.me/blog/posts/2019/new_in_ml/

What courses should I select?

It depends on how much you enjoy the theoretical aspects and mathematics. There are also many aspects of programming /technology / software engineering that do not require lots of maths - software engineering (frontend - making web apps, backend - ), UI/UX (User Interfaces, User Experiences on an app / website/ product), and roles that might not require programming - Product Manager, Business Analyst.

Don't be dissuaded if you are a social sciences student - there are many people who have went on to pursue careers in technology.

If you want to understand the underlying data science theory beyond a conceptual level (i.e. "this is how it works"):

mathematical foundations: A background in Linear Algebra and Multivariate Calculus (in MA100, MA212) and Statistics (ST100, ST202) is helpful programming: Familarity with one programming language (Python/R) will be very helpful. The LSE Digital Skills Lab offers instruction in Python/R, and there is a new half unit course in 2020: ST101 - Programming for Data Science

Regression is a really important topic and powerful tool, LSE has an unique advantage in that it has a world-leading economics department - and good courses on Econometrics - statistical methods in Economics to understand causal effects.

Regression (Statistics) :

ST211 - Applied Regression (Half Unit) - regression in R, more applied
ST300 - Regression and Generalised Linear Models (Half Unit) - the theory of regression and generalised linear models, an extension of linear regression.

Econometrics :

EC220 - Introduction to Econometrics, introduction to Econometric theory using calculus
EC221 - Principles of Econometrics, introduction to Econometric theory using Linear Algebra (essentially a graduate level course)
EC309 - Econometric Theory - deeper dive into econometric theory
EC333 - Problems of Applied Econometrics - a look at the applications of econometrics to real world examples

If you are interested in more advanced statistical courses:

ST304 - Time Series and Forecasting (Half Unit) - how to model a variable over time
ST308 - Bayesian Inference (Half Unit) - statistical inference from the Bayesian perspective (as opposed to frequentist of ST202) - Github
ST310 - Machine Learning (Half Unit)

If you are interested in more 'project' based courses:

There are also more domain-specific courses, such as:

FM320 Quantitaitve Finance - the MT content is about risk management, and covers time series modelling for risk (EWMA, GARCH, DCC)
ST326 - Financial Statistics - covers a range of topics in statstics that are relevant to finance - portfolio optimisation, risk modelling, machine learning

Other Courses:

MA314 - Algorithms and Programming, MA320 - Mathematics of Networks

Department of Methodology Courses:

Graduate level courses for Department of Methodology - but some of the content seems to be available publicly on Github

ME314 - Introduction to Data Science and Machine Learning https://lse-me314.github.io/
MY470 Computer Programming - Covers introductory programming in Python - https://github.com/lse-my470/lectures
MY472 Data for Data Scientists https://lse-my472.github.io/
MY452 Applied Regression Analysis https://www.lse.ac.uk/resources/calendar/courseGuides/MY/2020_MY452.htm
MY474 Applied Machine Learning for Social Science https://www.lse.ac.uk/resources/calendar/courseGuides/MY/2020_MY474.htm
MY457 Causal Inference for Observational and Experimental Studies - https://www.lse.ac.uk/resources/calendar/courseGuides/MY/2020_MY457.htm
MY459 Special Topics in Quantitative Analysis: Quantitative Text Analysis - Github Pages
MY461 Social Network Analyis - https://www.lse.ac.uk/resources/calendar/courseGuides/MY/2020_MY461.htm

Graduate level courses for Department of Statistics - some content seems to be available on Github

ST445 Managing and Visualising Data - Github Pages
ST446 Distributed Computing For Big Data Github Pages
ST449 Artificial Intelligence and Deep Learning https://lse-st449.github.io/
ST51 Bayesian Machine Learning - + Page

CV

It is recommended to keep your CV to 1 page, list your skills - programming languages and tools you are familiar with, internships, relevant programming projects (link to github if you have) - try and describe your contribution, impact using measurable statistics and what technologies used.

CV advice from reddit : https://www.reddit.com/r/cscareerquestions/wiki/faq_resumes

Interviews

The typical recruitment process might involve:

CV Screen, Coding test / take home, possibly video interview, in person interviews or "assessment centre" / on-site.

CV : Expect to be able to walk through and expand on your CV items. Anything you mention is fair game.

The coding test - Hackerrank, Leetcode or another platform - might involve testing your ability to use a language (sometimes you might not get to choose which programming language) to solve data structures / algorithms. This is to make sure you have degree of competence in programming. Sometimes data science questions might be asked, sometimes mathematical / brainteaser type questions. Sometimes it might take the form of a short project you have to complete and submit.

The motivational / behavioural questions are more about soft-skills: why are you interested in the company, do you work well in a team, can you communicate concepts.

Expect more technical questions on the onsite.

Some Data Science interview questions: https://github.com/alexeygrigorev/data-science-interviews

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