Overview
- Introduction
- Tips for Aspiring Data Scientists
- Tips for Junior Data Scientists
- Tips for Senior Data Scientists
- Wrapping Up
Introduction
Networking. We’re constantly told phrases like "I’ve heard networking is useful, why don’t you try that?". Encouragements like this are well-meant, but not very useful. Most people understand the value of networking. Many data scientists are not hired through official job listings but through referrals and connections.
It can be tricky to understand how to network effectively. Some advice is outdated, some is not relevant for data scientists, and some is outright wrong. It’s easier to look back at the previous step in your career and understand what actually worked. This is because you can see the common denominator between successful people around you ✨
In this blog post, I want to give you my tips for how to network as a data scientist. Below I’ve divided this into tips for aspiring data scientists, junior data scientists, and senior data scientists. I’m a senior data scientist myself, so I have experience with all of these steps. I’ve also talked with many professional recruiters and been on both sides of hiring interviews for data scientists. Still, please take everything I say with a grain of salt. I’m no more an authority on this topic than anyone else, and these are just my own reflections.
Before we start, you should know that I think approaching networking as an optimization problem is fundamentally flawed. Having 1000 LinkedIn connections or 5.000 likes on your post about LLMs is not the goal. The goal of networking is to establish genuine connections with other like-minded people that love the same things you do. These connections give you a network of people that you can both help and reach out to when needed.
Paradoxically, approaching networking as an optimization problem makes you bad at networking. This makes you focus on metrics rather than people. Meeting someone that only converses with you for the "clout" is off-putting. This approach is also phycological damaging. You begin to see people as mere means to achieving some arbitrary metric. This is neither particularly fun nor effective 😔
So while reading the tips below, don’t forget that networking is fundamentally about people. This, I’m sure, sounds about as cliché as it gets. But it can also be surprisingly calming. You don’t need to put on a salesperson persona for networking. You don’t need to wear suits and shoot finger guns while talking to new people. Your passion for Data Science is more than enough.
Tips for Aspiring Data Scientists
I’m glad you are interested in pursuing a career in data science. I will assume that you are not currently working as a data scientist, but want to do so in the future. You are probably studying at a university or a similar institution. At this stage of your career, I would focus on the following five tips for networking:
Tip 1: Take relevant courses to meet likeminded people
I’m sure courses in archeology or environmental law are interesting. Yet, you will probably not meet many future colleagues in such courses if you aim to be a data scientist. If you take courses in data science, statistics, or informatics, then this will be much more likely. The main reason to take relevant courses is of course to gain knowledge, but don’t underestimate the value of forming a solid network.
When taking relevant courses, try to talk to different people and get to know them a bit. You don’t need to be incredibly social if you don’t want to, but a little goes a long way. Having formed connections with 5–10 people for each relevant course you take quickly adds up.
Tip 2: If possible, apply for a (paid!) summer job that is relevant
If you are able to work a summer job as a data scientist, then this is immensely useful for gaining connections. Some people use this to get a foot in the company and often get a job there after they have finished studying. Even if you don’t, then having previous professional experience with data science is a big plus on any CV.
For the networking aspect, working at a summer job will introduce you to mentors and colleagues that you can connect with. People will have real-world knowledge of data science, which is often quite different from the academic version. Forming a network here is crucial for your future career.
When working in the summer job, try to get to know people outside the little bobble you will be put in. Have lunch with designers and chit-chat with product owners. Being in a summer job means that most people there will be very open to you asking questions and connecting. This makes the networking aspect a lot easier 😃
Tip 3: Go to career days and talk to representatives
Often universities will organize career days where companies come and give presentations. This is a great opportunity to talk with representatives from companies and get to know what they are all about. You might even find yourself some free food, which is always a plus as a student.
I know from experience that attending career days is a bit awkward. It’s semi-formal but also relaxed at the same time. Don’t worry. The representatives of the companies often feel the same way. When attending career days, make sure to make the most of it.
Tip 4: Enroll in a mentorship programs
Universities often have a mentorship/alumni program. There students can connect with professionals in the field to learn about the day-to-day work of the profession. This is a great opportunity that is most likely free and will introduce you to working professionals.
Mentors can give you tips on technical stuff, but also give you advice on how/when to apply for positions or which positions are good. Professionals who mentor students like this are usually doing it since they love their profession, and know it inside-out. If you decide to participate in such a program, then make sure to come prepared. Have questions you want to ask ready and respect the time of the mentor 🙏
Don’t be afraid to stay in touch with your mentor. Mentors usually love to hear how things are going and can give you more tips if you wonder about something. Later in your career, you can "give back" and become a mentor for someone else if you want to.
Tip 5: Have at least a minimal online presence
You should sign-up for the most used online networking tool for data scientists in your country. For most of us, this will be LinkedIn, so I will just refer to LinkedIn from now on. From talking with recruiters, I know that not having a LinkedIn profile at all is somewhat looked down upon. While I don’t necessarily personally agree, this hardly matters.
Add people on LinkedIn that you meet through classes, meetups, and summer jobs. This is a convenient way to stay in touch with them. You can also use LinkedIn to advertise that you are looking for a job when that time comes. Many of my friends have found jobs in this way.
Remember that you don’t need to post anything you are not comfortable with online. In fact, I don’t really think you need to post anything at this stage of your career. Simply create an account, add the relevant information, and stay in touch with people you meet. Simple as that!
Tips for Junior Data Scientists
I see you are working as a junior data scientist. Congratulations! It’s an exciting field, and I’m glad you are with us. You have a lot to learn technically, but paying some attention to networking is also good. At this stage of your career, I would focus on the following five tips for networking:
Tip 1: Cultivate good relationships with colleagues
This one is almost too obvious. You will most likely be working with other data scientists, data engineers, data analysts, and ML engineers in your career. Make sure that you have a good relationship with them and connect with them. Talk about data science and related disciplines by all means, but don’t shy away from talking about other things as well. Building a relationship with someone is usually easier if you know some simple things about them personally.
I would recommend getting to know people in other departments as well. When you change jobs, you might find that someone in your previous communication department works there now. They can still give a recommendation based on your personality, even though they probably can’t say much about your data science skills. Talking to people from different disciplines is also a great way to avoid being completely insulated. Some of the best data scientists I know have minimal working knowledge of nearby fields.
If you are working remotely, this will be significantly more difficult. I’ve worked remotely myself, so I’m not discouraging remote work as a practice in general. I just want to be honest about the reality of the situation if you choose such a position. If you do choose a remote position, then you can put more work into the next tips 💪
Tip 2: Attend meetups, workshops, and conferences
Outside of your job, there will be opportunities to meet other data scientists. This usually comes in the form of meetups, workshops, and conferences. All of these are good to attend from time to time to network. If they are good, then you might also learn a lot. While meetups and workshops are often free, conferences usually have fees that are quite expensive. You can ask your company to cover this.
I would advise you to only go to such events if they genuinely interest you. There is little point in you going to a React workshop if you are not interested in front-end development. Find something that genuinely interests you – otherwise, you will just be exhausted.
Finally, if you find a meetup that you like and have attended a few times, don’t be afraid to volunteer to present. People who organize meetups often struggle with finding presenters. You don’t need to be a senior data scientist to give a good talk. In fact, some of the best talks I’ve seen have been from less experienced people.
Tip 3: Don’t be afraid to mentor others
You might think that you are still not at the level where you can mentor others. But this is not true. Even someone who has only 6 months of working experience can mentor a new graduate on some things. Think back on when you started your job. What would have been helpful to know? Maybe it’s something as mundane as where certain information is located. Or maybe how data science deployment works in the organization? This might be easy for you now, but challenging in the beginning.
By Mentoring others, you form a connection with them. They can come to you with questions, and you will try to assist them. It’s not surprising that many new employees think of their mentor (given that the mentor actually does their job) as the person they know best. So ask your manager if you can be a mentor for new employees as soon as possible 🔥
Tip 4: Write blogs or teach a course on technical topics
You are in a position now where you can teach others. While mentoring as mentioned above is a very personalized way of doing this, don’t be afraid to do it through blogs or courses as well. If you write a blog post on a technical library that lacks good documentation, then many people will be grateful. Just make sure to pick a topic that you are comfortable with.
My tip here would be to start small. Are people in your company complaining that the new graduates don’t know version control software like Git? Offer to give a 1-hour course on it. Are colleagues complaining that it is difficult to understand when to use Pandas vs Polars vs Spark for data processing? Create a blog post for this where you take the time to investigate the differences. This can be valuable and great practice as well.
Tip 5: Improve your online presence
I think it can be beneficial to up the game of your online presence a little at this stage of your career. Don’t worry, you don’t need to become an opinioned data scientist influencer. But putting a bit more effort into your profile and interactions is probably a good thing.
For your profile, add more detailed job descriptions, skills, certifications, and so on. If you want to work towards becoming a senior data scientist, then emulate such profiles. Have a clear picture and an inviting and professional about-me section. This should not take you many hours to write, but many recruiters care about this. You can do better than having a picture of your cat as a profile picture and a description that reads "I do that data stuff."
If you want to post content, then post about interesting technology, interesting talks or blogs, or anything that genuinely interests you. You don’t need to make posts addressing current hype trends in data science or make clickbait posts with titles like "SQL is dead!". It’s not, and you hopefully know that by now 😅
Tips for Senior Data Scientists
Congratulations on becoming a senior data scientist! I’m sure that you’ve worked hard for this. This change in seniority also changes how you network quite a lot. In short, the major change is that you are expected to "put things out there" in various ways. In this stage of your career, I would focus on the following five tips for Networking:
Tip 1: Present at conferences, meetups, webinars, seminars, etc.
In the previous stages of your career, you mostly attended things. While this is still a valuable way to network, it pales in comparison to presenting. When presenting at various avenues you are indirectly showcasing your knowledge. If you do a good job, people will come to you. This reverses the polarity of your networking – you now have a pull-effect. Just know that presenting well takes a lot of time:
- First of all, it takes years to become good at presenting for most people. Start early. The earlier in your career, the more accepting people are of mediocre presentations. As a junior data scientist, people will be impressed with your dedication even if you presentation skills needs refining. At a certain point in your career, being awful at presentations is a lot more looked down upon.
- Secondly, even when you are pretty good at it, preparing for a presentation takes a lot of work. Hence you should select carefully when to present at an event. It is better to present 5 times a year excellently than to present 50 times mediocre. Prioritize quality over quantity.
Tip 2: Improve your writing
You will find that there is a lot of writing in your job. Whether this is documentation, memos, instructions, presentations, summaries, point-of-views, blog posts, emails, or something completely different does not really matter. The point is that writing has become something you do daily. And you are suddenly expected to be good at it.
The fact is that you will be judged on your writing. As such, it can be a serious impediment to networking at a senior level. For some it seems very shallow to judge someone on how they write. But the writing is often the only output the receiver can judge or even understand. Most stakeholders do not understand your clever hyperparameter search or your intricate data pipeline. If you can’t explain in simple terms why a project should be continued, then don’t act surprised when it is cancelled 😲
Having clear written communication is increasingly important for networking in particular. You will talk to many people through chat, email, and similar interfaces. If you write poorly, then this can sour the impression you give out.
For many of us, English is not our native language. Hence we often have two (or more!) languages that we need to communicate clearly in. This sounds daunting. Luckily for us, writing clearly is mostly language independent. Start working on the language you use the most in your networking. You will quickly find that the clarity of your other languages improves in parallel.
Tip 3: Build something of value
One of the ways you can prove your skills in the eyes of others is to build something of value. Whether this is a Python library that implements a new ML algorithm or a GitHub repo that demonstrates a cool use case is entirely up to you. It can also be an internal tool in your organization.
I want to emphasize that it is not always necessary to build something with great originality. Maybe your organization uses poor data pipeline orchestration routines. Taking ownership and fixing this brings value, even though it has been done 1000 times before in other organizations. Try to create something that will genuinely help others.
If you make internal tools, then this will gain the respect of others in your organization. If you make open-source tools, then this will help a wider audience. Both of these options are great for networking. If people already have a good impression of you, then networking becomes a lot simpler.
Tip 4: Become an expert on something
When starting out as a data scientist, you want to learn a bit about everything. This ranges from NLP to cloud platforms. As a senior data scientist, you should be knowledgeable of a broad range of topics. But you should also become an expert on something. This can be something technical like anomaly detection. It can also be more soft-skill based, like how to successfully implement agile methodologies in data science.
By becoming an expert, you become sought after because of your specialized knowledge. This opens many doors for networking, as you will be asked to speak about and write about your expert knowledge. Picking a specialized topic can be difficult. My best suggestion is to deep dive into something you really care about. If the topic is applicable in a wide range of settings, this is also a plus.
When networking, you can leverage your expert knowledge to stand out. Almost every data scientist knows a bit about deep learning. But how many data scientists do you know that is an expert in ensemble methods? Or an expert in bringing long-term organizational strategies with data initiatives? This stands out 😍
Tip 5: Have a strong online presence
As a senior data scientist you should aim to have a strong online presence. This includes engaging more with others and spending more time putting your ideas and opinions out there. There are many ways to do this, and you get to decide how. The format could be everything from LinkedIn posts to video tutorials. Focus on making quality content that really engage and illuminate topics. Again, remember that quality trumps quantity.
People often speak of developing your own brand. This term is a bit loaded and I prefer to talk about developing your own voice. When you write and talk, you will need to make some choices. Will you put on a friendly and helpful voice, or a more authoritative one? The choice of voice should be paired with what you want to achieve:
- Say you want to hire more junior data scientists for the team you have started to lead. Then putting on a "get off my lawn" type reactionary voice online is probably not a good idea.
- Say that you want to convince the world that Rust is the future of data science. Then putting on a friendly and agreeable voice is maybe also a mismatch. You should never choose an abusive voice, but sometimes a tint of pent up passive aggressiveness is entertaining 😤
Wrapping Up
In this blog post, I’ve given you my Tips for networking for the various stages of a data scientist. I don’t want you to treat this as gospel, but rather as pinpoints for what you could be working on.
If you are interested in data science then feel free to follow me on LinkedIn. But to make the connection meaningful, then please tell me a personal opinion of yours when it comes to networking. I’d love to hear what you have to say 😃
Like my writing? Check out some of my other posts for more content:
