What appears to be a relatively simple question is much more complex than you may think, but you don’t have to be one of the world’s best analysts and data scientists to get it right. Here’s our guide to the best things to include in your LinkedIn profile if you are working in data science.

Your LinkedIn profile represents your career, but it’s not your CV and shouldn’t be treated as such. A CV should be very detailed but also succinct and concise, including your core capabilities, skills, responsibilities, achievements and education. For example, 3 or 4 bullet points for a role you’ve been in for five years isn’t enough on your CV.

However, on your LinkedIn profile, it could be. LinkedIn is a searchable platform – recruiters, networkers, and businesses can all search for you. If your skill set is relevant to someone, but your LinkedIn profile isn’t up to date – you’re missing opportunities. In the world of big data and data analytics, machine learning algorithms are helping search functions, and it’s the same for LinkedIn, meaning your dream career, a volunteering opportunity, or a business opportunity could be easily missed.

Your Profile.

Job Title

If you are a Data Analyst, don’t claim to be a Data Scientist. Your job title should reflect the term for your role used most generally in the industry. Some companies have weird and wonderful names for roles, but publicly, your title should reflect your role in the wider marketplace.


It’s your choice here, whether you live, work, or want to be based if actively looking to relocate. Either way, radial searches are used by all organisations to target you for their goods, services or community activities.


Keywords are critical here, as is pinpointing your core capability and getting across your personality. If you work on sexy data products with huge amounts, data say it. If you do predictive modelling, data analysis, and visualisation, ensure you get this in. List keywords in terms of technology and resources you use and are capable of. The first line is important – You must drop down to read more, so make your headline stand out. E.g. “Machine Learning expert with x years of experience as a Data Scientist.” You get the gist.


Now it’s important to keep this up to date with accurate dates, information and company names. Here you talk about your role as a Data Analyst or Data Scientist and what you have achieved. Do not copy and paste your job description. For example, If you’re a data scientist heavily involved in linear algebra, machine learning algorithms etc., and you analyse raw data, it’s crucial to expand that to the techniques you use, the libraries you’re most familiar with, for example, if you used TensorFlow or built a multivariate decision tree then you should say so. You then lead on to outcomes, what was the tangible business benefit of the activity as an example, Data Analysis of Raw Data probably isn’t enough detail.

Also, you’re a Data Scientist; you solve problems and just saying so isn’t enough; what did you and how did you do it? Do you work on artificial intelligence? Do you employ deep learning techniques, and does your activity underpin business decisions? Research is critical, but so is commercial experience and detailing it is valuable to your LinkedIn profile.

Don’t fall down here with poetic licensing; be honest. If Python is your main language for data analysis, then make it clear, but if you know a little bit of R, only list it if it’s relevant. Likewise, if you’re involved in engineering tasks like data processing or data wrangling, it’s important to be clear about how much of it you do. We see too many ‘data scientists doing 90-100% data engineering tasks.


This is straightforward, as LinkedIn populates most of the fields you enter for your course. If you did something super niche at Uni with a PhD with a super long title, it’s a good idea to make it relatable, including your publications, research etc.


LinkedIn has a great feature where you can list your projects and link them to the company you achieved them at. Machine learning project? Add it. AI / Deep learning project? Add it. Statistical analysis and predictive modelling project? You guessed right; add it. It’s a great way to briefly showcase your achievements in a commercial setting. You should also add your academic projects if they are relevant to your career as a data scientist, including brief details of techniques and technologies.

Skills and endorsements

A critical part of your LinkedIn profile is that it is a searchable field, so if you list too much, you’ll appear in irrelevant searches, not list enough, and be missed. This is simple: list your core business and technical skills, like Python, R, SQL, data science, data analytics etc., as well as your business skills, stakeholder management, presenting etc. Only list your additional skills if you are confident and capable of doing them in a day-to-day role.


Get them. Simple as that. Ask people for them, and get them to recommend how clean your code is, how articulate you are at present, and how good your data visualisation skills are. Anything anyone can recommend because these go down an absolute treat whether recruiters are looking for you or an events firm looking for their next speaker.

So, there is a rundown on what your Data Science LinkedIn Profile needs to include. Not just a computer science discipline, Data Science is driving business decisions. If you want to stand out, you need to make sure your profile is up to date, lists your skill sets and articulates your actual experience clearly. Machine Learning and Artificial Intelligence are growing and evolving rapidly; make sure people know you’re at the forefront. Also, ensure your ability to communicate with the business is clear, whether through data visualisation or strong presentation skills; it’s a must-have for any Data Scientist.

Data science is just one of our specialisms here at Agile Recruit.  If you are looking for a fulfilling career in data recruitment, then talk to the specialist team at Agile Recruit. We have offices in both Manchester and Milton Keynes.

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