The Legend of Jeff Jonas | Jeff Jonas on The Artists of Data Science Podcast

The Artists of Data Science
5 min readAug 22, 2020


On this episode of The Artists of Data Science, we get a chance to hear from Jeff Jonas, a data scientist who, for over three decades, has been at the forefront of solving complex big data problems for companies and governments.

His software has helped casinos identify fraud, increased voter registration, protected Singapore’s waterways from piracy, and even predicted possible collisions between 600,000 asteroids over 25 years.

Jeff shares with us his journey from creating word processors in high school to being able to sell one of his companies to IBM, along with being one of three people to complete every Ironman triathlon in the global circuit.

This episode is packed with advice, wisdom, and tips that will motivate you!

Some notable segments from the show

[10:27] Jeff’s journey from high school dropout and bankruptcy to finding success

[17:24] Advice on taking entrepreneurial action

[23:04] The importance of curiosity and creativity

[24:42] How Jeff began his Ironman triathlon journey

[31:44] Advice for people trying to break into the data science field

Where to listen to the show

Listen to the episode on Apple Podcasts, Spotify, Overcast, Stitcher, Castbox, Google Podcasts, TuneIn, YouTube, or on your favorite podcast platform.

Jeff Jonas on the need for entrepreneurs to develop products with high margins

Jeff’s journey into data

Jeff’s journey began when his mom introduced him to computers when he was 14 years old. This sparked his fascination with computers and data, and this carried over to his high school years, where he wrote word processors. He was able to make some money off of them, and that instantly hooked him.

[4:08] “ I was like 16 years old and somebody sent me money for writing software. And I thought, this is crazy. And I was hooked. It’s just been an obsession my whole life.”

Where is the field headed in 2–5 years?

In Jeff’s opinion, the field is actually beginning to flat line in certain areas. For example, it is going to take more than five years until we have autonomous cars on the road.

The biggest challenges we currently face are related to security and privacy. For instance, the number of ransomware and phishing attacks in public and private institutions has gone up tremendously. With this in mind, Jeff thinks we have our hands full on helping secure our systems.

[6:07] You know, it’s been kind of flat lining, I think, for a while now. Like, you know, it’s it’s showing a lot of utility against pictures and and and like multimedia sound data, but it’s not really continuing to have the same gains and other kinds of prediction areas, and it tends to flatten out early. You get all these early gains. But then to get to the last mile, it’s not… It’s been a little tougher.

What will separate great data scientists from the rest of them?

The great data scientists will be able to converge multiple data sets. They will be able to gather secondary data from secondary sources, and weave together this data for more effective outcomes.

[8:08] I think that a lot of the big gains to come are not about pointing algorithms at a data set, but converging multiple data sets and getting orthogonal data like secondary data from secondary sources thinking about it like a puzzle. You’ve got red puzzle pieces, blue, yellow, you know, green, white, black, brown, all these color puzzle pieces.

Key takeaways from the episode

How to be an entrepreneur

Another area to focus on as a data scientist is any type of fraud, waste or abuse detection product. This is a great place for data scientists and machine learning to find patterns in the data to quantify fraud and stop it.

[20:19] If you’re an entrepreneur and want to be successful in the next couple years, focus on building worker productivity tools. If you’re creating something and it doesn’t give somebody a really fast return on investment, it’s going to be very, very hard to sell because companies right now are scrambling to reduce their costs.

Important soft skills

[23:04] Do not underestimate curiosity. Knowing where the data is and how it’s structured, how it flows and how to combine it, are very important soft skills to cultivate.

Word of encouragement for new data scientists

[31:44] Download some data, and start. Just work on stuff, even if it’s free, just to get your hands on real problems and real data.

Working on projects that have utility

[35:50] Focus your efforts on projects that have utility and are sustainable for society. Don’t be directionless with the projects you choose to work on.

Being accessible

[49:57] It’s important to be accessible. You can learn a lot by connecting with others. It also creates a lot of goodwill.

Memorable quotes

[15:46] “For everybody that’s had a close call in life…every day since then has been an extra day. When you think about life like that, it allows you to just unleash a little bit more and make the most of it…”

[31:01] “…You have to let new observations reverse earlier assertions.”

[34:31] “If you don’t have something that’s like 10 times better and high margins, then you can’t innovate”

[43:03] “…My work is often about helping humans focus their finite resources”

The one thing that Jeff Jonas wants you to learn from his story

[32:44] If you quit, there’s no chance a miracle will happen.

From the lightning round

Best advice

If you want to do really high quality work and build a reputation, you’ve got to really deliver on what you promise.

Advice to 18 year old self

Stay focused on things that are useful and sustainable.

Recommended books

What motivates you?

Right now, I’m just trying to make a difference, especially in this COVID world. I’m trying to make sure my team is doing well and their families are doing well.

Books and other media mentioned in this episode

Where to Find Jeff Online

Personal Twitter

Senzing Twitter



Episode transcript

You are welcome to share the below transcript (up to 500 words) in media articles (e.g., The New York Times, LA Times, The Guardian), on your personal website, in a non-commercial article or blog post (e.g., Medium), and/or on a personal social media account for non-commercial purposes, provided that you include attribution to “The Artists of Data Science” and link back to the URL.

For the sake of clarity, media outlets with advertising models are permitted to use excerpts from the transcript per the above.

The transcript for this episode can be found here.

The full episode with Jeff Jonas on YouTube