Survive and Thrive in Your Data Science Career - A Meetup Summary

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Posted: 2021-01-31

Intro

As a co-organizer of Seattle Artificial Intelligence Workshops Meetup group, on January 23rd, 2021, I had the pleasure to host, along with co-host Kendall Chuang, the following rockstar speakers for a panel on surviving and thriving in DS.

  • Carol Willing (Noteable)
  • Jennifer Kloke (LinkedIn)
  • Chuck Cho (FaceBook)
  • Katherine Lin (Microsoft)

We asked each of the panelists 5 questions pre-selected from attendee questions that were submitted before the meetup. As this wasn't recorded, I place a list of takeaways that stood out to me from the panelists answers below. If you attended this meetup or a similar one, feel free to add your takeaways in the comments section.

Takeaways

  1. One of the most important questions to yourself: "Do I want to do this and why?". Then, just be yourself.

  2. One of my favorite things said was learn to do more with less data. To me this means getting creative with data exploration and ways to merge and enhance it. Also, this statement reminds me to engage in the careful study of approaches that can handle smaller datasets, which could involve cutting edge research. Sounds like fun and a good excuse to read papers or pay attention to Papers with Code!

  3. On the future of AI I heard three main topics as callouts from the panelists: AI infrastructure, security and explainability. These are fields and topics to look out for as machine learning and data science matures and could evolve into future roles.

  4. To take that next step in leadership, rather than thinking solely of the promotion and getting ahead, take the approach of simply trying to make the life of your manager easier.

  5. Get sponsors and mentors outside of your company. For example, ask people to give career guidance and/or look at your work to provide feedback. This will enhance your network and provide new perspectives.

  6. One piece of advice on staying on the cutting edge is to attend poster sessions at conferences (it may be harder in a pandemic, but perhaps look out for lightning talks as well) and ask questions.

  7. When learning, focus on quality resources. I like to find classic text books that are also enjoyable reads. Others may take online courses. Perhaps, find what other prominent DS folks recommend and try those paths out (or if they have a book or course they've made, perhaps pay attention to that).

  8. It was suggested to use GitHub like you would a portfolio. It's a great way to showcase work and you can add links on your resume. Ways to use GitHub include:

    • Showcase a cool project publicly - maybe even something you are currently learning (it's ok to learn in public)
    • Place any work-in-progress in a drafts folder if you wish
    • Add an informative and compelling readme, but note, it doesn't have to be professionally done (it can evolve with your project)

  9. These were skills mentioned and recommended for being successful in DS:

    • SQL
    • Python/R/Julia
    • Stats
    • Linear Algebra
    • Data structures and algos (i.e. fundamentals of CS)

  10. Here are some resources mentioned:

In Conclusion

It was a wonderful experience hosting these folks and we hope to do this kind of event again soon! If you have suggestions on an event like this, feel free to comment below.

Also, I suggest that you follow Carol on Twitter at @WillingCarol, Chuck at @cycho and Katherine at @Katheri79597873.

Happy travels on your data science journey. You can be a DS rockstar, too!