13 Lessons from DSGO 2017’s Discussion Pannel

Something is probably missing… but what? What should you do to take your career to the next level?

Last year we hold a standout data science conference called DataScienceGo. It left us with many valuable lessons, and one of the most fruitful parts of the conference was the discussion panel. We had the opportunity to discuss quite a few career-related topics with the well-recognized professionals in data science.

In the following article, we want to share with you the biggest insights from this discussion panel.

Check them out! Because the chances are high that these ideas will be very handy in boosting your career in data science.

DataScienceGo is a standout data science conference. What makes it special?

First of all, it’s a career-oriented event. You can meet here lots of inspirational speakers who provides dozens of tips and tricks on advancing your career in data science. What is more, DataScienceGo is also a great source of technical knowledge and business soft skills. And of course, it provides huge networking opportunities even for newbies in data science area.

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The speakers from all over the world with different experience and from different backgrounds share the things that make them successful and that you can take back to the work that you’re doing.

The discussion panel last year was especially powerful in relation to practical advice and insightful discussions. The panelists were:

• Hadelin de Ponteves, Data Science Entrepreneur,
• Ben Taylor, Chief AI Officer and Co-founder at ZIFF, Inc.
• Urie Suhr, Director of Talent Acquisition at Collective[i],

They were very honest when sharing their experience and expert opinions about the future of artificial intelligence, stepping into the data science career, working efficiently and delivering the value.

So, why not take a helicopter view over the main points discussed there?

No, AI is not hype. People are making lots of money thanks to AI. All these success stories are not hype but real evaluations and real revenues.

Of course, media is often misleading in relation to AI capabilities. There are a lot of talks about AI growing exponential, but this is not the case. At least, research in AI is not exponential.

You should work from a place where you feel most comfortable with the environment and are the most productive. And it’s not about your hourly efficiency, it’s about you weekly throughput.

If you’re choosing to work from home, remember to engage with your team – with Slack or any other tools that work best for you.

Once you built your reputation and you’re on top of the game, you can ask for whatever you want. You can say that you’re only coming into the office once a week or that you need four months of a ski vacation. It doesn’t matter.

If you’re offering the value, and the company wants your attention to its problems, you can demand lots of things. Of course, there are some traditional employers out there, who will see these requests as very, very strange, but if your employer is not working with you, there are lots of employers that will.

There is a great book called The Fourth Economy by Ron Davison. He talks there that traditional 9-to-5 job schedules came from the industrial revolution where the amount of time you spent at work was proportional to your output because you were on a conveyor belt.

Today it’s not about the time, but about the effort, creativity, and ingenuity that you put into your work. You can have specific hours through the day, when you are the most productive, and it would be great if you had the opportunity to set your schedule based on your productivity instead of traditions from times of industrial revolution.

As machine learning meets BI, it brings a lot of automation to the BI problems. DataRobot is a great example: you just give it the inputs and it gives you some outputs. So, more and more operators of BI software have AI on their roadmap.

Job posts for data science roles are open for 45 days, whereas the average is 40 days. This implies that there is an insufficient supply of data scientists. The estimates are that this imbalance will last for another 3-6 years.

In the coming years, the demand will be still increasing because more and more companies are becoming interested in the areas of data science and artificial intelligence. On the other hand, new software can easily substitute entry-level data scientists. So, today you need to become an expert in deep learning and state-of-the-art techniques to be confident about your job security.

It’s a huge plus to start a career in machine learning with a math major. You’ll have the opportunity to read and comprehend the best books, even highly theoretical ones. Likewise, once you understand the basics of machine learning, you can go to the research papers, which are usually full of math.

Math classes can be boring as long as you don’t realize its application to the real-world problems. But guess what? Linear algebra has some fantastic real-life applications in machine vision and image processing. So, math major is a great start!

From the technical standpoint, you should master deep learning and artificial intelligence. That’s our future. From a behavioral perspective, do things that you are passionate about, develop your analytical skills and business sense.

Business sense is not developed with courses and books but with work experience. You can join consulting companies or specific departments in your company where you will work on cost reduction and business optimization. You can definitely use data science to optimize business, and that’s a great way to develop the business sense.

However, not everybody in the team of data scientists needs business sense. Often, it’s sufficient that the group leader, i.e. chief data scientist, has a strong business sense. Thus, this is an important skill if you want to evolve in management positions.

When you are a fresh candidate, don’t care much about your first job, but think about the job you want to have in three years. Look for the job that will provide you with problem variety and will help you build the reputation.

Some data scientists regularly hit a wall where they can’t advance, while other data scientists blast through any brick wall even on things they haven’t been exposed to. If you belong to the second group, that’s something that employers really value. So, don’t hesitate to add to your resume: “I get stuff done”.

Finding information is also an important skill set. And as with the other skills, you become expert after you’ve lots of practical experience. So, become expert googler by googling everything you need!

It might be sometimes difficult to combine part-time data scientist role with some full-time position because you might lack the breadth and the depth that the full-time employees have with regards to technical skills.

However, freelance definitely counts as job experience. So, if you can show that you bill $150/hour for consulting, you can ask for that six-figure salary. And your background doesn’t matter as long as there are clients ready to pay you for solving their problems with data science.

As you see, the discussion panel covered diverse career-related topics from different perspectives. Check this podcast for the full discussion. It actually includes other topics on top of the ones overviewed above, not to mention multiple jokes and lots of fun we had during this discussion.

Are you excited about the possibility to ask YOUR question this year? DataScienceGo 2018 will take place on October 12-14th in San Diego, California. We will actually have two discussion panels this year: one related to emerging technologies and another one talking about women in data science.

Is diversity a key factor for business and career success? – Join our discussion panel to learn the answer!

Also, meet Hadelin and Ben from our last year discussion panel. They are going to give some inspiring speeches this year. And not only them but also many other exciting guests and speakers.

The conferences are great in many aspects: they provide network opportunities, they allow you to meet experts and influencers face-to-face, they give you some new tips and tricks, and of course, they are fun! Just make sure you are prepared to get the most out of the conference.

When visiting DataScienceGo, you’ll enjoy all the common benefits of attending industry conferences and communicating with like-minded people. And even more! Our conference is different from other data science conferences due to its clear focus on career development. We will be there to help you boost YOUR CAREER in such a fast-evolving field as data science.

So, what are you waiting for? Get your seat for DSGO 2018!