Tech, data and digital are our passion at Ignite. We build digital careers; helping to transform the business journeys of our forward-thinking clients. We find them the talent to propel their business to the next level.
The roles we place are diverse and far-reaching, but no role gets as much interest as that of a data scientist.
When we win one of these roles, we get excited! These are unicorn opportunities that don’t come around very often. Working with data is a vocation; once a candidate lands one of these highly prized roles they don’t want to move on!
Data science is also the field that we get asked the most about. Candidates, fresh grads, and master’s students reach out for guidance and advice about how to launch their data science careers.
With this in mind, we thought it would be helpful to address this in the next of our CV Essentials blog series. Are you writing your Data Science CV? Are you contemplating a career in Data Science? If so, this is for you.
To become a Data Scientist, you will typically hold a bachelor’s degree in computer science, social sciences, physical sciences, mathematics, or statistics.
The most common fields of study are mathematics and statistics (32%), followed by computer science (19%) and engineering (16%). A degree in any of these courses will give you the skills you need to process and analyse big data.
Data scientists are highly educated individuals. 88% have at least a master’s degree and 46% have PhDs. There are exceptions, but as a rule, a very strong educational background is required to develop the depth of knowledge necessary for a career in data science.
A solid formal education is only the start! Following graduation, aspiring data scientists should expect to complete significant online learning to develop their skills.
There are masses of online learning opportunities for data scientists with some of the best coming from the likes of Udemy, Coursera, Metis and edX.
Some courses are free, some carry a cost…however they need to be viewed as an investment. Continued learning is pivotal to a career in data science.
As a data scientist career progresses, it will have a growing impact on both the organisation and the projects worked on. Your data science CV should reflect your growing impact.
As you write your CV, keep in mind the following rule –
“The more senior a data scientist, the greater an impact they have”.
Even if you have remained in one organisation in the same role, the projects you list on your CV need to reflect your contribution and professional growth – even if your job title has remained the same.
For example, most junior data scientists will have limited experience and focus primarily on execution. With help and support, these individuals will then go on to produce higher quality work more rapidly.
A second-level data scientist will have more independence especially in writing code and performing analyses.
In your next project, you may have learned enough to be able to formulate unstructured problems. You may be able to identify the highest-impact problems, which you are then left to solve independently.
This trajectory of impact continues until you are working as a senior data scientist impacting most problems at company level.
As you progress and you are making more of an impact in the projects you work on, your CV will grow. These marked responsibilities should be listed and recorded on your CV so that you are able to show improved seniority within the role you hold.
There are several technical skills that should feature on a Data Science CV. The following may not be exhaustive, but they are a great place to start.
It is also important to add here, that you would need to be a superhuman to have amassed ALL of these especially in early roles! However, your CV should aim to include as many of these as possible.
R is an essential piece of know-how. R is specifically designed for data science needs and can be used to solve most problems encountered in data science. 43% of data scientists use R to solve statistical problems. However, R has a steep learning curve, with some reporting that it is particularly tricky to master if you have already learned a programming language. There are great resources on the internet to get you started in R. ‘Simplilearn’s Data Science Training with R Programming language’, for example, bills itself as the world’s number 1 online boot camp and has a 4.5 star review on Trustpilot.
Python is the coding language most typically featured in data science roles; 40% of data scientists use python as their chief programming language. Alongside Java, Perl, or C/C++, Python is a key programming language for data scientists to learn.
Python is incredibly versatile and can deal with almost all the steps involved in data science processes. For example, it can take various formats of data and users can easily import SQL tables into your code.
Although a familiarity with Hadoop is not always a CV requirement, it is often preferred by employers. A study carried out by CrowdFlower on 3490 LinkedIn data science jobs ranked Apache Hadoop as the second most important skill for a data scientist with a 49% rating. Having knowledge of Hive or Pig is also a strong selling point, as is a familiarity with cloud tools such as Amazon S3.
Your work as a Data Scientist is likely to plunge you into a situation where the volume of data you have exceeds the memory of your system or that you need to send data to different servers. This is where Hadoop comes in; it will quickly convey data to various points on a system. Additionally, you can use Hadoop for data exploration, data filtration, data sampling, and summarisation.
Even though NoSQL and Hadoop have become a large component of data science, it is still expected that a candidate will be able to write and execute complex queries in SQL.
SQL (structured query language) is a programming language that can help you to carry out operations like add, delete and extract data from a database.
It is important to be proficient in SQL because it is specifically designed to help you access, communicate and work on data.
It allows you to query a database and has concise commands that can help you to save time as it lessens the amount of programming you need to perform difficult queries. In short, learning SQL will help you to better understand relational databases and boost your profile as a data scientist.
Apache Spark is fast becoming the most popular big data technology worldwide.
It is a big data computation framework just like Hadoop, but it is faster.
Hadoop reads and writes to disk, which makes it slower, whereas Spark caches its computations in memory.
Apache Spark is specifically designed for data science to help run its complicated algorithm faster. It also helps data scientists to handle complex unstructured data sets. You can use it on one machine or a cluster of machines, making it an essential skill on the CV of a data scientist.
Machine Learning and AI
Although this is changing, many Data Scientists are not proficient in machine learning areas and techniques. To stand out from the crowd, it is beneficial to have machine learning techniques such as supervised machine learning, decision trees, logistic regression, etc on your CV. These skills demonstrate that you can solve different data science problems that are based on predictions of major organisational outcomes.
The business world is moving increasingly toward a data-first mindset. Data in volume is not something many people are able to interpret and understand.
As a data scientist, you must be able to visualise data with the aid of data visualisation tools such as ggplot, d3.js and Matplottlib, and Tableau.
An ability to use these tools will allow you to convert complex results to a format that will be easy to comprehend; most of us non-data folk do not understand serial correlation or p values. As a data scientist, you will need to convert the raw data into readable graphs, charts and representations that can then be used to exploit opportunities and stay ahead of the competition.
A data scientist must be able to work with unstructured data. Unstructured data is that which doesn’t fall naturally into database tables. This may include videos, blog posts, customer reviews, social media posts, video feeds, audio content etc. Sorting these types of data is difficult because they are not streamlined.
Unstructured data is often referred to as ‘dark analytics’ because of its complexity. Working with unstructured data helps you to unravel insights that can be useful for decision making…especially when it comes to unpacking customer experience. As a data scientist, you must have the ability to understand and manipulate unstructured data from different platforms.
As with many tech, digital and data careers, “soft skills” are overlooked in favour of technical ones. Data professionals have a natural acumen for numbers, statistics and large volumes of data. However, they also need to be able to communicate this into language which laypeople can use to develop the business.
A Data Science CV must demonstrate the following:
A strong data scientist is someone who can clearly and fluently translate technical findings to a non-technical team, such as the Marketing or Sales departments. Additionally, they must be able to understand the needs of their non-technical colleagues in order to wrangle the data appropriately.
A data scientist cannot work in a silo, and a great Data Science CV must reflect this. You should demonstrate where you have worked with company executives, product managers and designers to create better products.
It is important that you can demonstrate an amassed knowledge of the industry you’re working in. More specifically, you should be aware of the problems that your target industry may come up against and the role data will play in solving them. If you have published papers, contributed to industry literature your data science CV should include links to these.
Curiosity can be defined as the desire to acquire more knowledge. Data science is a field that is evolving very quickly; your learning should keep up with the pace. Your data science CV should be rich with side projects reflecting your interest.
Covid 19 has put physical meetups on hold over the past 18 months, however when it’s safe to do so, aspiring data scientists should take opportunities to network with other like-minded professionals. Not only is this a fantastic professional opportunity, but it facilitates learning by the sharing of ideas and the discussion of techniques.
Data Science CV Takeaways.
We hope this edition of CV Essentials has given you some guidance on the writing of your Data Science CV.
Here are the take-aways….
- Data Science roles are unicorn opportunities. They don’t come around often. Your CV needs to stand out.
- Data Scientists are highly educated individuals. A bachelor’s degree is a prerequisite and a master’s degree is highly desired. A PhD is also a common feature on a first-class Data Science CV.
- You will need to demonstrate intellectual curiosity by seeking out additional learning opportunities following graduation and beyond. There are masses of paid and free online courses which will facilitate additional knowledge and skills.
- Data Scientists can demonstrate their skills by including a wide variety of projects on their CV. Candidates should look to include as many relevant technical skills as possible as well as the “soft skills” which are critical, but often overlooked.
- The career history of a Data Scientist can sometimes appear static with not much upward movement. However, when detailing the projects you have worked on, don’t forget to mention the IMPACT your work has had. As a data science career progresses, the impact of the work will reach higher within the organisation. This will let your recruitment partner or hiring manager know your level of seniority.
Are you a fresh grad or an experienced data scientist looking for your next role? If so, why not head over to our job board browse through our data vacancies?