David Yakobovitch
The data science field is growing with companies hiring data scientists to assist in strategic management and decision-making. Businesses are turning to data-driven solutions¹ in marketing, customer service, and human resources to help them stay ahead of the curve. To achieve these goals, companies are hiring data scientists to support digital transformation.
In this new digital economy, demand for data scientists² is increasing. Some data scientists excel in their careers while others land in firms that do not value data science and this turns into a bad experience. Learning to market yourself as a data scientist is the first step towards a fulfilling career in data science.
What does your current skill sets³look like in data science? Ask yourself this question as you brainstorm how to brand yourself as a data scientist and learn which skills employers are looking for.
A compilation of job postings from Ziprecruiter, StackOverflow, and Indeed indicated Python, R, SQL, and big data as the top four desired skills by employers. Apache Spark, Hadoop, and Java are additional skill sets listed as the most desired in this list. Others included Scala, NLP, ETL, and deep learning.
When it comes to branding yourself as a data scientist, one-thing counts- Learning the best marketing strategies to navigate the data science field.
Let us explore some best practices for marketing yourself as a data scientist.
1. Start attending Hackathons
Branding yourself starts with learning new ideas and hackathons are a great starting place. By attending hackathons⁴, you will put your skills in practice, solve problems, and learn strategies for handling complex challenges. Teams at hackathons build products or solve a problem and through these collaborations, you expose yourself to the real world of data science applications.
Learning data science and becoming a skilled professional is not enough. Attend hackathons and start contributing. This will build your confidence as you advance your career and more exposure to new opportunities. Kaggle platform organizes hackathons where participants compete to solve problems with a prize for the winning team.
Hackathons are my favorite activity and even before the pandemic hit, I was attending hackathons where I led teams in building AI products⁵. I have seen incredible talents in these hackathons and this has been a learning experience for me. Companies such as Knotel and Waymo use hackathons as a benchmark for hiring data scientists and this should motivate you to attend hackathons.
2. Build Great Content
Content creation is a phase you should not miss out as a data scientist⁶. Most data scientists undermine the power of content and as the saying goes ‘Content is King’.
You need to develop a great content strategy if you need to showcase your skills to the world. Building a content strategy will attract interest from data scientists, companies, and new networks.
Thanks to social media, content creation is a walk in the part since you have platforms to post topics on data science. #Socialmedia channels including LinkedIn and Twitter are effective in sending your message out. The trick in social media is to understand the kind of content your audience needs and delivering exactly that.
Do not focus too much on increasing your audience but keep refining your content. As long as your content is solid, the audience will keep growing.
Millions of online users read blogs and watch videos on YouTube and represent new channels to create and share content. Top bloggers understand the power of blogging and keep their users engaged with exciting posts. This is the same for the data scientist looking to spread the word about their skills in the data science field.
The same applies to YouTube videos where users can subscribe to your channel. Both blogging and making videos require sacrifices and this will make a difference in marketing yourself.
Podcasting is another tool for content creation. Content does not mean written information alone and with podcasts, now you can create engaging audio discussions around data science.
I launched the #HumAInpodcast with a view of teaching audiences the latest trends in data science, artificial intelligence, developer education, and the future of work⁷.
Whether you are an Executive, data scientist, software engineer, product manager, or student-in-training, The HumAIn Podcast¹⁴ connects you with industry thought leaders on the technology trends that are relevant and practical. As the host of the HumAIn Podcast, I discuss topics such as human-centered AI, the fourth industrial revolution⁸, and technology start-ups.
3. Join Data Science Communities
Data science communities are growing with Kaggle and GitHub among some popular platforms. These communities share information and resources on data science that will help you get in line with the latest trends in the field. For instance, most #datascientists are familiar with Python language but the truth is that new languages such as R, MLR, and Tidy Verse are sweeping the industry as well.
My point is that joining these data science communities will expose you to the latest trends and networks you can use to build your brand.
Every data scientist needs to join GitHub⁹ for accessing resources, code, and learning about the data science profession. Resources on GitHub assist data scientists to navigate the field by learning from others and using the skills to advance their careers.
4. Speak at Events
Data Science events and conferences are taking place around the world and in the United States; conferences take place from New York, San Francisco to Seattle. These conferences offer a great opportunity to network with like-minded professionals in the industry and build your network.
Speakers in these conferences deliver speeches about the industry and current trends, which every attendee learns. Europe like the US is also hosting many conferences around, data science, artificial intelligence, and #machinelearning and the same is happening across Asia.
Landing a speaking opportunity in these conferences could pose challenges at first but patience matters as you build your networks. Collaborating and speaking with others during these events offers an opportunity to market your skills and within no time, you will be getting paid speaking opportunities. The World AI Congress recently held in China is an example of major conferences that data scientists should attend for them to become recognized.
As the world returns to a new normal after the COVID-19 pandemic, make a plan of scheduling your calendar to attend these events. The website KD Nuggets is one place you can find all conferences taking place in the USA on data science, machine learning, and #artificialintelligence¹⁰.
The good news about events as a data scientist is that organizing them is not a preserve of tech companies and start-ups like before. More conferences are springing up each year.
5. Become an expert in Data Science
You cannot brand yourself as a data scientist without a good knowledge base about the field. You should gain knowledge in all areas and keep becoming better through constant learning. Reskilling and upskilling is critical for every data scientist given the fast pace at which the industry is changing.
When was the last time you led a team to solve a problem related to data science? This question should motivate you to take that step and build something. When you start solving data science problems and developing applications, the industry learns about your skills and this raises you a notch higher. Data scientists with successful careers participate in product development and solutions. You should do the same to become an expert in data science.
This is true for me. I built a Data Science Standards¹² for my students where I teach them design thinking for the modern data scientist. For example, in the data science framework, I teach students 5 steps of design thinking which include:
Data collection
Data refining
Data expansion
Data learning
Data maintenance
Thanks to the Data Science Framework I created, I got more calls, emails, and speaking opportunities in New York, San Francisco, and around the world including Singapore and South Korea.
Learning and gaining knowledge in data science is good but developing solutions by solving problems is better. This is what makes companies and new networks to start noticing you.
Data Science Marketing is about Value Addition
The success of any marketing campaign lies in the value promised to the target market and delivering the right experiences. Steve Jobs applied the same mantra during his time at Apple and with core values such as excellence and consumer relationships, he transformed Apple into the world’s most valuable brand. The same is true for the data science field¹¹ where you need to become skilled and understand what value you can offer to the world.
Building a great network takes time and with the changes experienced in data science, you should brace yourself for a tough terrain. I do not mean to discourage you but rather motivate you to raise your game and approach the data science field with the right marketing approach.
Besides becoming a top expert in #datascience, all this will mean nothing with poor marketing tactics. LinkedIn is my favorite social media tool for marketing and this does not mean everybody should disregard other channels such as Twitter, Facebook, or Instagram.
As the principal data scientist at Galvanize¹³, I must say that progressing through the data science field is no easy road and requires adequate preparations. Not every data scientist becomes successful and developing a strategic marketing approach in your career is the best bet. This will make a difference.
Works Cited
¹Data-Driven Solutions, ²Demand for Data Scientists, ³Current Skill Sets, ⁴Hackathons, ⁵Building AI Products, ⁶Data Scientist, ⁷Future of Work, ⁸Fourth Industrial Revolution, ⁹GitHub, ¹⁰Artificial Intelligence, ¹¹Data Science Field, ¹²Data Science Standards
Companies Cited