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Reflecting on Data Science & Data Analysis Careers for Researchers

Isobel EPowell12 December 2019

Data Science & Data Analysis Month… let’s reflect:

After a busy month of events focused around all things data, we are reflecting on what it takes to excel. This industry is fast expanding with companies heavily investing in their data. The issue here then lies with know what role is suitable for you and where to start when currently (12 Dec 2019) there are over 2000 data scientist roles live on Indeed (indeed.co.uk). It is clear then our reflection this month should focus on what types of organisation could suit you.

Read on for our insights and what we have learnt from our employers this month…

Data Science in Start ups

If you want to get stuck in with some real hands on experience of data looking at start ups could be for you. The roles will require:

  • more commitment to the company and the role
  • longer hours especially around peak funding cycles
  • less role structure so tasks could be adhoc and change daily

but the increased learning and development opportunities could be appealing for you:

  • Working in smaller teams you get more responsibility
  • You could gain a better all around knowledge of data
  • and experience various different parts of data

You will however be required to have more skills going in and be expected to have a better all around knowledge from sourcing, cleaning and presenting data. Job security and longevity is a something to be aware of as work loads tend to cluster around these key funding cycles.

Data Science in Large Organisations

The big four, the banking sector and consultancies are not immune to the data boom. Roles in these organisations are:

  • highly sort after in the graduate market
  • come with a more competitive and rigorous recruitment process
  • open doors and offer global opportunities

Working life may be secure and hours more regular however this sector is notorious for:

  • increase pressure from client projects with higher workloads
  • more corporate structure
  • Projects set by management or clients so less autonomy

Often working within a team of engineers, analysts and other data scientists who are specialised in various areas means your role will be more specific maybe focusing on data preparation, visualisation, machine learning, analytics or pattern recognition. These roles are high paid but also high workloads so investigate first and gain some practical advice first.

Data Science in the Public Sector

Whilst still a large, national organisation, the healthcare, government and education sectors have working styles, they are often:

  • restrictions by laws and high scrutinised
  • have lower budgets and must show real value for doing anything

Despite this, a role in the public sector could afford you:

  • Increased intellectual freedom and better understanding of your research background
  • being treated more like a researcher, investigating trends and potential to publish
  • More flexibility with better working structures and regulations

If you’re looking to make change to the way our public services are run and improve communities through research, a public sector role in data could be for you, creating and presenting information from data which shows critical issues and opportunities for development.

So, what does this all mean for you?

The top tips we gained from our panellists and employers focused on ensuring in applications that as a researcher you prove, what your data expertise area, what is your area of interest and how can you benefit an organisation.

Key advice to get you started:

Use the software – Practice it! If you’ve got an industry in mind, research what tools are most used and up skill yourself on these. Whether that be Java, Python, C++ or Matlab.

Show what you can do – Share it! There are tones of great website where you can upload data examples to prove your skills. Why not start a blog showing your research process or create a profile on an online community – examples included Kaggle, CodeWars, WordPress or Stack Overflow.

Get some real experience – Prove it! Reach out to companies and see what opportunities there are for you to support them, maybe as an internship, a project or a part-time job. If you’ve got the skills and time to support your career development then gaining corporate experience could improve your chances.

Grow your network – Pitch it! Found a perfect organisation? Or an alumni whose transition out of academia is inspiring? why not see if they have time to share some tips. This could be a great opportunity hear about unpublished opportunities and gain insights.


Finding an industry where your skills as research are valued and utilised may seem tricky but you can find roles across all sectors and industry. This is where our themed months come in to play, if you’ve decided health organisations are not for you, join us on another themed month and hear more about careers in Data Science & Data Analytics, Communications and Research, Government, Policy and Higher Education…. the list continues!

Come along to our events and find out how your skills are so transferable across the sectors and explore how you could branch out to support an organisation to develop!

Check out our full programme of researcher events on our website today!

What is Data Science and how can you get into it? Tips from a Data Scientist

Vivienne CWatson1 April 2016

Shaun Gupta has a MSci in Physics from UCL and a PhD in Particle Physics from Oxford. He tells us how he started his career in Data Science and what being a Data Scientist is like.

GuptaTell us about your job.

I am currently employed as a Data Scientist at a startup called Row Analytics. Data Science is an emerging field, and it involves using a mixture of coding and statistical analysis to answer questions using big datasets. The company is very small (less than 10 people), which means my role actually covers a wide range of different activities in addition to just Data Science. It is an exciting place to work as I am helping to build the company from the ground up, in a sector that is still relatively new and constantly evolving.

How did you move from a PhD to your current role?

After undertaking an MSci in Physics at UCL, I pursued a PhD in Particle Physics at the University of Oxford. During my final year, I spent a month taking part in the 2015 Science to Data Science (S2DS) bootcamp, based in London.  The school was a pivotal opportunity to learn more about the emerging field of Data Science, and showed me how relevant my skill set was in industry. As part of the school, I spent time working on an exciting project with my current employer Row Analytics, who offered me a full-time position once the school was over.

What does an average working day look like?

As the company is currently very small, I tend to perform a variety of tasks as part of my job. My time at the moment is split between helping to set up an infrastructure in the cloud on AWS, setting up and configuration databases (noSQL and graph based), building a web application (both front and server backend), writing programs to scrape unstructured data, and performing Natural Language Processing (NLP) on the data.

How does your PhD help you in your job?

In essence, my role is very similar to what I did during my PhD, only using data from a different source. As a result many of the techniques and practices I learnt during my PhD are useful in doing my job. These include programming, problem solving skills, strong mathematical skills, statistical analysis techniques including knowledge of learning algorithms, and the ability to work independently in a research driven way to develop new ideas/products.

What are the best things about your job?

I enjoy working in a constantly evolving field with many opportunities to get involved in new projects and learn about new cutting edge techniques. I also find it exciting working for a company at such an early stage in its development, and being involved in shaping its future. I am also lucky in how flexible my work is, with the ability to work from home a couple of days a week.

What are the downsides?

As the role involves a lot of coding, a lot of time can be spent fixing bugs. Also a lot more time is spent working with the data and structuring it in the correct way for analysis than one may expect initially.

What tips would you give researchers wanting to move into the same, or similar, role?

Data Science is a new and exciting sector that is rapidly growing, and so now is the perfect time to get involved. I would say solid programming skills coupled with good analytical ability is key. Therefore, I would advise to brush up on your coding skills in languages such as Python and R, and your knowledge of statistics. Attempting challenges on sites such as Kaggle is a useful way to do this. Attending a school such as S2DS will help you to learn more about the industry, and get involved in real world applications of Data Science with companies. There are also many meet-ups around London and boot-camps that are worth attending.