Getting your first Data science role

“Data science” is a relatively new field that combines knowledge of statistics, machine learning and programming in solving real world problems using data. The field is still very new and whilst many companies understand why they need data scientists, many don’t know what skills they should be looking for in potential hires. A lot of data science job descriptions have a long list of skills which suggests that companies are looking for a “unicorn” with all the possible combination of skills required (or perhaps they don;t know what they should be looking for). However, in reality, most of these unicorns do not exist and its often more practical to build a data science team with individuals who have strengths in various domains of datascience.

Broadly speaking, data careers can be divided into data science and data engineering. Data science is more to do with analysing, visualising and deriving meaningful insights from data. Data engineering on the other hand is more concerned with building data pipelines to deal with large datasets, etc. Data engineering is more related to software engineering whilst data science is more suitable for candidates from physical sciences background (including maths, physics, quantitative biology/neuroscience, computer science, engineering, etc). In reality, this distinction is often not clear on job descriptions. Also, due to a shortage of talents, intersections between data science and engineering is quite common. See the four types of data scientists for more.



How do you land your first job as a data scientist?

Getting that first job as a data scientist can be a very important first step in your journey to becoming a competent data scientist. It can very challenging especially if you’re coming straight from university with little “real world” experience. It must be said that a PhD qualification alone will not give you an automatic entry into a data science role-you will have to demonstrate your capability as a data scientist. having said that, there are various routes to becoming a data scientist and you don’t necessarily need a PhD to get into one but you do have to demonstrate a breadth of skills.

  1. PhD route: A common route for landing a data science role is through a PhD qualification in a quantitative/scientific field such as physics, mathematics, engineering, neuroscience, biology, bioinformatics, computer science, etc. There are bootcamps that specifically recruit PhD graduates where they get to work on projects attached to companies either alone (ASI Fellowship) or in collaboration with other graduates (Science to Datascience). Some popular bootcamps include the ASI fellowships2ds and insight fellowship  programmes (note that the first two are based in the UK whilst the third is in the USA). For a more comprehensive list of data science bootcamps, see the following link.
  2. Masters/undergraduate route: Data scientists can be hired straight after an undergraduate or masters degrees into entry level data science roles. Its important to demonstrate your competencies through projects and if possible by doing an internship with a data-driven company. If going via the masters route, I would recommend a masters in machine learning. There are a few masters in data science but I feel these are probably too broad and non-specific.
  3. Portfolio/work experience route: This may be suitable for individuals who are already in industry and are wishing to move into data science roles. People in this group may typically come from analyst, software engineering, business intelligence roles. The key is to develop a portfolio of data science projects which can be uploaded to github, etc for employers to see. Other ways to show competence include contributing to data science open source projects.


So the question is: How do you land that first job?

  1. Know the basics of data science theory very well. This includes mathematics/statistics (linear algebra, calculus, numerical optimization, regression, algorithms, etc), programming (at the very minimum-python or R), machine learning algorithms, visualisation, some familiarity with big data tools (scala, spark). If interested in Data Engineering, its crucial to get familiar with the big data tools-scala, spark, hadoop. As a general rule, its important to be comfortable with at least one of the data wrangling tools (R or python)-this means at least 10,000 hours of coding in that particular language.
  2. Demonstrate your interest by undertaking a data science project in your spare time. Find a question that you can address using online data and showcase your work on a github account. If you’re already studying for a masters or a PhD, try to demonstrate your data science interests through your projects.
  3. If possible get some relevant industry experience related to data science and to the industry of your choice. Domain knowledge is crucial in being an effective data scientist. This can be acquired through an internship, kaggle competition, hackathon or through previous work experience.
  4. Network within the data science community by attending meetups, conferences and arranging meetings with data driven companies of interest.
  5. Keep up to date with the field by reading new articles, publications and algorithms. I personally follow data elixir, and other twitter accounts of prominent data scientists.


Lastly (and it goes without saying), apply to roles of interests. Some roles are not widely advertised and this is where networking becomes important. There are several roles out there but its important to scrutinize job specifications carefully to ensure they are truly datascience roles. All the best and watch out for my next article on how I made the transition to becoming a data scientist.


Career options for maths PhD graduates

The aim of this blog is to educate Maths PhD students (as well as their lecturers, career counsellors and the general public) on potential careers in industry available to them. The good news is that maths/STEM PhDs are in great demand in very attractive careers.

There’s no doubt that a vast number of career options are open to graduates with mathematical talent and education. A good example is this list of 85 job descriptions of mathematicians working in industry. I like this list because it gives concise descriptions of the various roles and as such gives a great insight into the skills required to do them. For this reason, finding the right career can seem like “finding a needle in haystack” situation. This is the main reason why this blog was created.

To further complicate this, I have noticed that most mathematician roles in industry rarely carry the title “mathematician”. They are often called various other names including but not limited to the following:

1. Business analyst
2. Software Engineer
3. Computer scientist
4. Research associate
5. Data scientist
6. Operations researcher
7. Hydrologist
8. Basin modeller
9. Geologist
10. Statistician
11. Actuary
12. Cryptographer
13. Quantitative analyst
14. Financial Engineer

This is hardly surprising as applied mathematicians are so versatile and often find themselves in roles that are traditionally occupied by other science, technology and engineering graduates alike. Consequently, the general public do not have a true appreciation of what mathematicians really do apart from the glaringly obvious teaching of mathematics. The job titles are often a reflection of the work environment a mathematician may be working rather than the background/skills required to get the job done. Not surprisingly, a quick search on google looking for “mathematician jobs” in industry may not necessarily yield very much. My first advice for maths PhD graduates is not to dismiss any role based on its job title but to pay particular attention to the job description before making a decision. As a maths PhD student myself, I will be sharing my own discoveries which I hope will be of some use to other research students. I plan to update this page on a weekly basis so please do visit again for additional information.

1. The first resource I’m going to recommend can be found on the society for industrial and applied mathematics website. This website is packed with lots of useful resources on destinations of maths PhD students and a lot of case studies of mathematicians working in industry-definitely worth a look. You can also download their brochure and read in your spare time. It seems to me that a mathematician is a true jack of all trades and master of all! Alongside this, I would also highly recommend the American society of mathematics website.


2. This website is packed with lots of quality information about various career options for maths graduates. Although, some of the first few links are no longer working, don’t be put off by this. I really recommend the financial mathematics section for those interested in this line of work.


3. Internships: An internship experience can serve as a straight entry route which allows you to explore a company and indeed secure that dream job. Most internships for PhD students are paid and these are usually offered in the penultimate year. For example, here is a list of companies offering PhD students internships in quantitative finance. Some companies however offer off cycle internships meaning you can apply anytime even after completion of your PhD.

4. This is a general website from the university of manchester containing adverts for jobs outside academia-very useful if you don’t know where to start from.

List of Companies specifically recruiting PhD applicants

A Phd in mathematics is a very valuable qualification to have on a CV. However, as valuable as it is, not all companies specifically recruit PhD talents into industrial roles. This unfortunately means PhD graduates may have to compete with Masters or even undergraduates (very annoying if you ask me). Hence, it is worth targeting companies with specific roles and schemes for PhD graduates (Google, McKinsey and Co, PWC, companies recruiting quantitative analysts, pharmaceutical companies, data analysis and tech companies, GCHQ, etc)

All in all, I think it’s important to really make a list of skills that each PhD graduate feels they can offer and target companies who are after those skills. For example, google is very keen to employ PhD graduates who have strong skills in software development, machine learning and statistics.