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A career in cyber security

By uczjsdd, on 7 January 2021

Dr Ardavan Alamir has a PhD in Physics and now works in cyber-security at G-Research. We caught up with Ardavan to hear about his role and career journey so far.

Tell us about your job.

I work as a Tech Lead Cyber Data Scientist at G-Research, a fintech organisation. Cyber security is very important for the business. The cyber function collects a huge volume and diversity of data, big data. So my role is to help the cyber analyst sift through the data quicker with the help of data science. One particular area of focus is the use of anomaly detection to find unusual signs of activity that we could be indicative of a cyber attack.

How did you move from academia to here?

I finished my PhD and I was looking for a PostDoc. The search didn’t go well. Then a friend who works in Big Data and data analytics strongly encouraged me to switch to Machine Learning. I studied the famous Andrew Ng course on Coursera. It was brilliant. Then I started to apply and I found my first job in an Education Tech company.

What was the recruitment process like?

Interviews have 2 main focuses: technical and behaviour. For a data scientist role, a lot of the interviews will revolve around Machine Learning, Deep Learning, Probability/Statistics, SQL queries and Computer Science Data Structures/Algorithms. So you need to make sure you learn the key topics of these fields. You have a lot of sample interview questions online. Great resources are glassdoor, leetcode.com and interviewquery.com. The behaviour part is about finding out about you as a potential colleague.

What does a normal working day look like for you?

I start my day with a team meeting at 9am. In the morning I try to do some personal learning that will be useful for my work. Then I work on a current project. I also have regular meetings with my fellow team members to discuss progress with current projects.

What are the best bits?

Getting to do cutting edge research and getting paid much more than in academia!

What are the biggest challenges?

he worst bits in the corporate world, especially in bigger corporations, is the politics. You have Game of Thrones all the time, especially for employees at manager level or above. When you stay in a technical role, it shouldn’t concern you too much though you will witness it.

Is a PhD essential for your role?

No, but what the PhD helped me with is the ability to do effective research in a new area, see a project through its end, and computational and analytical skills.

What top tips would you pass on to researchers interested in this type of work?

Definitely do some online courses on Machine Learning, Stats and Algorithms/Data Structures. These are the 3 key areas where most companies will interview candidates. I would even say that acquiring knowledge in these three areas is more important than doing personal projects. And definitely have passion !

Reflecting on Data Science & Data Analysis Careers for Researchers

By uczjipo, on 12 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!

Welcome to Careers in Data Science & Data Analysis

By uczjipo, on 2 December 2019

Data Science & Data Analysis Month

Interested in data? Have a passion for exploring information or creating solutions? Ever thought of using your skills in data management, coding or analysis as a career? Join us for Data Science & Data Analysis month to find out more about this fast-growing and lucrative industry. Come along to our employer taster session and test ut your data mining skills or attend our employer and alumni forum to hear more about the industry, its scope and the specialisms you could go into.

Thinking about attending but not sure if it’s for you?

Come along if you want to learn more about how to use data in a career or just hear from speakers with research and PhD backgrounds who have transitioned out of academia. Improving business practice, creating important analysis or implementing processes your thing? A career in data could allow you to expand the reach of your research and support an organisation to grow in so many different industries.

If you want to transition out of academia but still support research, come along to our sessions and hear how this is possible with a career in data. 

Heres what’s coming up…
Check out the events coming up this month and learn more about this expanding and research-focused industry. Understanding the meaning behind data is becoming central to all business practice. This is why careers in data span across retail, finance, government, education and more.  Check out what’s coming up and explore a career in something new! 


Careers in Data Science & Data Analysis Forum
Thurs 5 Dec, 5.30-7.30pm

Skills in research, analysis and data presentation are vital to the data science industry and are why increasingly organisations are looking to hire researchers.

This forum will give you the opportunity to get an insight into the data science and data analysis sector from PhD level speakers who have paved a career for themselves in this industry. Find out more about what a career in data encompasses, the wide range of industries and specialisms this covers and gain tips on how to find a researcher role. This is a key opportunity to gain an insight into a career you may not have previously considered.

Speakers include:

Dr Lucie Béraud-Sudreau
“Dr Lucie Béraud-Sudreau is Research Fellow for Defence Economics and Procurement. She studied international affairs at Sciences Po Bordeaux (France) and holds a PhD in political science from the University Paris-2 Panthéon-Assas (France). Her PhD thesis compared French and Swedish arms export policies since the end of the Cold War. Lucie’s current role involves, inter alia, data collection and analysis, catering and updating datasets on military expenditure.”

Dr Liam Duguid
“Liam has been worked in data science for the last 4 years and just moved organisations from Capita to HCL. His role has focused on data preparation, algorithm design and machine learning development along with implementation. The new role will be using these skills and others such as natural language processing to solve problems in Data Science Consultancy. He previously completed a master in Theoretical Physics at UCL followed by a PhD at Royal Holloway in High Energy Particle Physics working on dielectron decays in the ATLAS experiment at the LHC.”

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Employer Taster Session in Data Analysis – Led by Celonis
Mon 9 Dec, 12.30-2pm

Please bring your laptops!

Process Mining – Understanding The Story Behind the Data
Validating meaning behind data patterns can sometimes be tricky. Very often we can identify trends but struggle to understand the root causes and the story behind them. Process Mining is a useful method to reconstruct the as-is process behind the data and use sequential analysis of activities over time to visualise bottlenecks and deviations based on data stored in IT systems.

With its academic DNA Process Mining has not only turned into a budding research field but also transformed the way companies operate. The talk will cover an introduction to Process Mining both from an academic and applied perspective, its application in data-driven management today as well as a hands-on case study in the software. The session will also include insights into the unicorn story of Celonis.

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What else can you do to get career ready?

Alongside this, we have a team of careers consultants with research backgrounds who work closely with UCL’s researcher community and can provide support regardless of whether you’re looking to continue in academia or explore other options. Our “Researcher appointments” can be booked at any time through your myUCLCareers account and can be used to cover a range of queries from exploring options to getting support with applications/interview preparation. The careers consultants also run separate workshops covering a range of topics on academic and non-academic career routes for researchers.

Details of the full events programme can be found here

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

By uczjvwa, on 1 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.