10 tips and tricks for a good intern job application

Posted by on May 16, 2022

We’ve recently completed the hiring for this year’s cohort of summer interns and as a result we’ve processed hundreds of applications; just across our data teams we had over 600 this year! For many of these applicants, who tend to still be at university or have recently graduated, this is likely to be one of the first ‘professional’ roles that they are applying for. As such they might not be aware of tips and tricks that make a good application, specifically in the tech industry. So, I thought I’d tell you about a few of the things you can do that make applications stand out from the crowd when applying for an internships.

What’s the FreeAgent application process? 

To set some context, the application process for internships at FreeAgent is essentially a scaled down version of the interviews we carry out for full-time positions, which has been covered in this blog post on the data science hiring process. Our intern application process is as follows:

  • CV and cover letter screening
    • To check that the candidate has the skills and passion that match up with the job
  • Technical task
    • A task that mirrors the work the candidate might do as part of the internship 
  • Interview
    • To get to know more about the candidate and test out their communication skills 

Keep your CV relevant

I’m going to be honest with you – when screening CVs we can’t spend quite as much time as we’d like looking at each in detail. With hundreds of applications to go through it really does just have to be a screening. Therefore it is vital that your CV is easy to read and well laid out with clear sections so that your experience and your skills stand out.

CV tips 

  • Try to limit your CV to two pages; less is definitely more 
    • You don’t have to list every job and every school qualification you have. Only include ones that are relevant to the type of role you are applying for 
  • List your relevant skills in a separate section 
    • When looking through CVs I tend to look for certain skill ‘buzzwords’ that are vital to the role. Having these listed separately makes it easier to spot them. For example, programming languages (SQL, R), software packages (MATLAB), learning techniques (statistical analysis) and relevant qualifications (Google Analytics Individual Qualification)
    • It’s also highly valuable to give some examples of when you’ve used these skills. Tell us what the impact was, and why your contribution influenced it. It really helps to paint a picture of your experience 
  • Outline any degree modules you’ve done that are relevant to a tech role
    • Whilst you might not have real-life work experience in using tech to solve problems, you’ve probably had some practice of it at university either through dissertations, personal projects or similar. Outlining the most relevant ones, including any skills you used/gained during it, will still be useful in helping to understand your experience

Your cover letter is just as important as your CV

Always, always, always write a cover letter. Even if it says it’s optional. Some hiring managers will disregard applications immediately if a cover letter isn’t supplied. Cover letters don’t have to be a full page, they can just be a few paragraphs, but it shows that you’ve put the effort in, which makes you stand out from those who didn’t. The cover letter is your chance to sell your application and gives you the opportunity to say why you’re the best candidate for the role. It should ideally show that you have an interest in, or have at least researched, the organisation you’re applying to. 

It also tells the recruiter what your written communication is like. With more and more roles now becoming remote, a lot of communication is going to be written. The cover letter gives some indication that you can translate your ideas and thoughts across this way.

Cover letter tips

  • Outline the skills you have that match the job role
    • Don’t repeat everything that is in your CV, but just highlight a few of your key skills that the job is looking for. For example, if the role is looking for someone who has programming experience, list the languages you’re skilled in and include an example of a time you’ve used them
  • Mention why you want to work for the company
    • Be specific about why this company stands out to you and why you’d want to work for them. It might be that you’ve read really good reviews on Glassdoor or that you’ve read about the work they do and you’d like to be a part of it. Many tech companies have websites where they blog about their work – you can check out our previous intern blog posts here. These are good resources to look at when applying for jobs 
    • Your cover letter should be tailored to the role and company you’re applying to and shouldn’t be generic. Include some specific details you’ve learned about the company rather than something that can be applied to most companies such as “you care about your employees” 
  • Don’t be afraid to add a bit of personality to your letter 
    • It’s nice for us to get an idea of the person behind the letter, whether it’s through your writing style, or a story you tell that gives us an idea of your experience

PDF everything

It’s such a simple thing to do but not doing it could hinder your chances from the get-go. Whilst your cover letter might look fine on your laptop in Microsoft Word, someone else opening it on a Mac in Google Docs might have difficulty with the format. This is when things go a bit skew-whiff, and your CV can become almost unreadable. In the past when this has happened to us we’ve reached back out to the candidate to ask if they want to resubmit their CV in a PDF format, however not all hiring managers may be as considerate. 

Formatting tips

  • PDF all documents that you’re submitting as part of your application
    • This can include your cover letter, CV and any additional work you might have to supply as part of your portfolio 
  • Save any files using your name
    • When you save your files as PDFs it’s helpful to include your full name in the file name. This is so when a recruiter accesses them it’s easy for them to know exactly whose work they’re looking at, and it doesn’t get mixed up with all the other applicants

Always refer your skills back to the role

Job applications commonly have some additional questions that are asked to help the recruiter understand a bit more about you. For example, one of the questions we ask is, ‘Tell us about a recent project you’ve worked on which you’re really proud of’. This doesn’t have to be a project that is related to the specific role we’re recruiting for, it can be about anything. However, it’s a good idea to tie this back to the role you’re applying for if possible. For example, were there any learnings from you project that you could transfer to the role? This helps to emphasise what skills you can bring to the role, ultimately showcasing how you’re a good candidate for the job. 

List your assumptions for the task

If you’re given a task or exercise as part of your application, then it’s not uncommon to have some questions, or even to be unsure of the wording of the task. Sometimes this is on purpose. Don’t be afraid to reach out to the recruiter to ask them for confirmation of what something means. Or ask if you can use any specific software to help you complete it. When you’re completing a task, always list out any assumptions you have made. Even if you may have interpreted the question wrong, understanding your thought process and the methods you used to tackle it is still very relevant for the recruiter.

Summary

So, those are my top tips on what makes a good intern application but they will be relevant for any job that you want to apply for. I hope you find them helpful in your quest to find a job in the tech world.

Finally, if you’re successful enough to progress through to an interview then you might want to check out this blog post on how to prepare for a tech job interview – there are some really useful tips on how you can set yourself up for success. 

Happy job hunting and good luck! 

The three skills you need to be a Data Analyst, and how to get them – Part 3

Posted by on May 10, 2022

We recently advertised a Data Analyst role, which had the following desirable skills listed:

  • creating and querying data models using SQL
  • working with both structured and semi-structured data
  • exploring and visualising data
  • using probability and statistics to perform and support analyses
  • drawing insights from large and complex datasets
  • using hypothesis tests to create rigorous insights from data
  • working in an agile manner to continuously deliver work
  • articulating results to a broad range of audiences

We didn’t expect any candidates to tick every single box, but the most compelling applications at the first stage ticked a number of them.

An oft neglected skill

Most data jobs will measure some element of data engineering and data analysis, as I talked about in part 1 and part 2 of this series of blogs. This blog is more concerned with a (so-called) soft skill, which I believe is absolutely essential: “Data Evangelism”.

Data Evangelism

You’ve shaped and analysed your data – now you need to get someone to take notice! This requires good written and oral communication skills, and the ability to work closely with people on problems they face, so you can help identify where data might help solve or illuminate a problem. Amazing and insightful analysis is worthless if there is no action taken off the back of it.

How does it relate to desirable skills?

In our job ad, the last two bullet points were the Data Evangelism skills we were interested in:

  • working in an agile manner to continuously deliver work
  • articulating results to a broad range of audiences

How to learn Data Evangelism

This one is harder to learn in a concrete way, and to a large extent you will get better with experience and practice. That being said, there are definitely concrete things that you can do in order to develop your skills in this area:

What do you need to know?How can you learn?
How to tailor your language and presentation to the audienceAt FreeAgent, we speak to lots of different audiences about data and concepts, from small focused meetings of 2 or 3 people, to Town Hall meetings of over 200.

The key is to be able to tailor your language to the audience, and don’t bombard people with thousands of charts and images – try to make your data “tell a story” – only show what is necessary, and avoid jargon where possible.

If you search online for “data storytelling”, lots of blogs and articles come up, and also a few books. Storytelling with Data, by Cole Nussbaumer Knaflic isn’t one I’ve read (yet!), but has received excellent reviews and endorsements.
How to work collaborativelyWorking with data is about more than sitting on your own “crunching numbers”. You need to be able to understand “what”, “how” and “why” in order to have a meaningful impact.

That means talking to people and listening to what they say, asking the right questions and translating the answers to something you can work with. The Art of Statistics by David Speigelhalter has some fantastic early chapters on how data analysis starts with understanding the problem and then planning, before even looking at data. It’s fantastic reading for understanding how to, erm, understand.

I hope you enjoyed reading about Data Evangelism and why it is important. If you didn’t already, check out my blogs on the skills needed to become proficient in Data Engineering and Data Analysis – happy job hunting!

The three skills you need to be a Data Analyst, and how to get them – Part 2

Posted by on

As mentioned in the first blog in the series, we recently advertised a Data Analyst role, which had the following desirable skills listed:

  • creating and querying data models using SQL
  • working with both structured and semi-structured data
  • exploring and visualising data
  • using probability and statistics to perform and support analyses
  • drawing insights from large and complex datasets
  • using hypothesis tests to create rigorous insights from data
  • working in an agile manner to continuously deliver work
  • articulating results to a broad range of audiences

We didn’t expect any candidates to tick every single box, but the most compelling applications at the first stage ticked a number of them. We covered the first couple of bullet points in the prior blog, which I referred to as “Data Engineering skills”. This blog covers the second skill we look for: Data Analysis.

What is Data Analysis?

The name of the job! Once we have our data, we want to analyse it to produce meaningful insights. This is all about having the knowledge and skills to explore the data and identify patterns/trends – you’ll want to be sure that what you’re saying is justified, and so knowledge of statistics is crucial.

How does it relate to desirable skills?

In our job ad, the four middle bullet points were the Data Analysis skills we were interested in:

  • exploring and visualising data
  • using probability and statistics to perform and support analyses
  • drawing insights from large and complex datasets
  • using hypothesis tests to create rigorous insights from data

How to learn Data Analysis

There’s lots covered in the desired skills, and lots to learn. Some of the books and links below are a great starting point:

What do you need to know?How can you learn?
Working knowledge of statistical principlesIn order to perform exploratory data analysis and perform rigorous, meaningful analyses, a working knowledge of statistics is necessary.
This can be done via book learning, but many online “Data Science” courses introduce some of these ideas.

There are some good Data Science courses at DataCamp, which let you choose between python or R (we use both!), which also cover some of the basics.

Delphine from our Data Science team recommends this Udemy Data Science bootcamp, which is reasonably priced and very thorough. (You can read more about how Delphine moved from lab scientist to data scientist, here).
How to visualise data to ease interpretationAside from the technical details of how you visualise data, the goal of doing so is to make it easier to interpret. This helps people quickly answer: What is this data telling me?

To do this as well as possible, it’s important to understand how humans interpret visual information – read this paper by Cleveland and McGill to learn more about that.

How Charts Lie by Alberto Cairo will also provide a good grounding in visualisation.

Hopefully you’ve already read my blog on how to get started with Data Engineering, there’s just one more skill to look at: Data Evangelism, and you can read all about it, here.

The three skills you need to be a Data Analyst, and how to get them – Part 1

Posted by on

We recently advertised a Data Analyst role, which had the following desirable skills listed:

  • creating and querying data models using SQL
  • working with both structured and semi-structured data
  • exploring and visualising data
  • using probability and statistics to perform and support analyses
  • drawing insights from large and complex datasets
  • using hypothesis tests to create rigorous insights from data
  • working in an agile manner to continuously deliver work
  • articulating results to a broad range of audiences

We didn’t expect any candidates to tick every single box, but the most compelling applications at the first stage ticked a number of them.

The skills we looked for

When writing the job ad, there were really three areas we wanted the “desirable skills” to describe. They include “Data Analysis” and “Data Evangelism”, but this blog covers the first of the skills: “Data Engineering”.

What is Data Engineering?

Data Engineering is all about working with and structuring data. A large part of most data roles is sourcing, cleaning and shaping data so that it is fit for analysis or further use. We are no different, and we build and maintain multiple datasets.

How does it relate to the desirable skills?

In our job ad, the first two bullet points were the Data Engineering skills we were interested in:

  • creating and querying data models using SQL
  • working with both structured and semi-structured data

How to learn Data Engineering

Now you know the required competencies, I have outlined below where you can go to learn them. I hope you find it useful!

What do you need to know?How can you learn?


How to work with structured data:

i.e. data stored in a relational database


To work with structured data, you need to learn SQL, the language used to query databases.

Search for “learn SQL online”, and there will be lots of courses that will give you the basics. Many courses are free, but charge for completion certificates (which I don’t consider as necessary).
How to work with semi-structured data:

i.e. data (usually) not in a database, but with some form of tagging to allow the data to be described, such as a JSON or XML file		It’s harder to find an online course for this specifically, but many of the courses listed below under Data Analysis will cover elements of this (e.g. you’ll use the Pandas read_json function in the python courses).

You could also have a look at some of the Kaggle challenges, or find/build your own dataset and perform an analysis. For instance, smart watches, Fitbits and the like have a wealth of data – can you shape and analyse that to find something of interest?

If you want to move into data, at some point you’re likely to come across Python, as it is a common language used to either process and analyse data. The 100 Days of Python course will help you go from “absolute beginner” to “extremely competent” in Python, and should help you write good quality code while enhancing your “data engineer” skills at the same time.
How to model data:

this means the best way to shape and store data based on its expected use		For a more general appreciation of data structure, it’s a bit more complex. Structuring data for OLTP vs a data warehouse is a very different proposition.

Some of this comes with experience, and through doing SQL courses you might pick up on things as a side benefit.

However, there are definitely patterns and anti-patterns that can be learnt. I think The Data Warehouse Toolkit by Ralph Kimball is very good from a data warehousing perspective (sorry Inmon fans!), but be warned – it quickly gets technical, and implicitly assumes prior database experience.

Once you have started Data Engineering, don’t forget to check out the next blog in the series covering a skill so vital it’s in the job title: Data Analysis.

We're totally hiring!

See our current engineering vacancies