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Michael Segala. He\u2019s the CEO and co-founder of a company called SFL Scientific, a data science consulting firm that specializes in big data solutions. He\u2019s for leveraging machine learning in analytics techniques to arrive at insights to numerous industries\u2014 from healthcare to stock market predictions. Before founding the company, Michael worked as a data scientist in some of the well-known companies such as Compete Inc., Akamai Technologies and he also holds a PhD in Particle Physics from Brown University.

Famous Five:

• Favorite Book? \u2013 The Challenger Sale
• What CEO do you follow? \u2013 Larry Page and Sergey Brin
• Favorite online tool? \u2014 Slack
• How many hours of sleep do you get?\u2014 6
• If you could let your 20-year old self, know one thing, what would it be? \u2013 \u201cDiversify my education, learn more than just science from the early set, it will help you out\u201d

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Time Stamped Show Notes:

• 01:09 \u2013 Nathan introduces Michael to the show
• 01:56 \u2013 The founding members of SFL Scientific are particle physicists
  • 02:41 \u2013 They have a deeper understanding of the problem\u2014from the academic and business perspective
• 02:58 \u2013 SFL Scientific is completely bootstrapped with $2K as their initial funds
• 03:07 \u2013 SFL Scientific got their first client only a few weeks after their launch
• 03:24 \u2013 The first client was a group of people from Stanford studying sleep apnea
  • 03:30 \u2013 Sleep apnea is a disease that makes you stop breathing for a couple of minutes while sleeping and can lead to death
  • 03:46 \u2013 The group\u2019s idea is to take the sound and record it through an iPhone app at night
  • 03:59 \u2013 The group hired SFL Scientific to build an entire suite of AI machine-learning product solution
  • 04:04 \u2013 SFL Scientific also got an FDA resolution for the product
• 04:30 \u2013 SFL Scientific is a complete professional-based consulting firm
  • 04:40 \u2013 They write specific algorithms for the clients depending on their needs
• 05:18 \u2013 SFL Scientific got their first client in 2015
• 05:24 \u2013 Michael is now 31
• 05:44 \u2013 The pricing depends
  • 06:17 \u2013 For a high-level R&D-based projects, the charge is hourly
• 06:34 \u2013 SFL Scientific does R&D-based projects with minimum requirements
• 07:10 \u2013 Most clients don\u2019t understand the scope of the project so SFL Scientific asks business questions or strategy
  • 07:45 \u2013 SFL Scientific provides the possible end result
• 08:08 \u2013 First year revenue is low 6 figures
• 08:27 \u2013 SFL Scientific has 3 co-founders
  • 08:38 \u2013 Michael does more on the sales stuff such as talking with client, one handles the technical and the other handles the implementation of behind-the-scenes coding
  • 09:14 \u2013 Equity is almost equal with Michael getting 34%
  • 09:37 \u2013 The first 2 years, they invested back into the company most of what they got
  • 09:53 \u2013 They had some very low salaries
• 10:27 \u2013 SFL Scientific almost broke a million in 2016
• 10:42 \u2013 2017 revenue might go over and above a million
• 10:57 \u2013 Team size is 10
• 11:30 \u2013 SFL Scientific currently has a dozen clients
• 11:38 \u2013 One of the clients takes up around 20% of the revenue and Michael knows that it is dangerous
• 12:00 \u2013 SFL Scientific has no churn yet
• 12:08 \u2013 SFL Scientific mitigates a couple of ways the employees can work on multiple projects at a time
• 12:24 \u2013 SFL Scientific doesn\u2019t invest only in one problem\u2014go vertical to diversify the risks
• 13:12 \u2013 Looking at data science in general, the challenges are unanimous
• 13:34 \u2013 SFL Scientific is capable of understanding and solving cases from different industries
• 14:07 \u2013 Nathan just finished Thinking in Systems
• 15:48 \u2013 If you don\u2019t have decent data to support a model that is accurate to a certain degree, you\u2019re not going to get anywhere
• 17:03 \u2013 SFL Scientific looks at the potential of a project
• 17:16 \u2013 Michael is most excited with the health industry in terms of AI and machine learning
• 19:15 \u2013 The Famous Five

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3 Key Points:

1. Consider yourself lucky when you\u2019re completely bootstrapped and you end up getting your first client only after a few weeks of launching.
2. It\u2019s quite risky to only solve one problem as a company; diversify your services so you have a greater chance of surviving.
3. Study different fields and see how you can solve cases from these different industries.

Resources Mentioned:

• The Top Inbox \u2013 The site Nathan uses to schedule emails to be sent later, set reminders in inbox, track opens, and follow-up with email sequences
• Klipfolio \u2013 Track your business performance across all departments for FREE
• Hotjar \u2013 Nathan uses Hotjar to track what you\u2019re doing on this site. He gets a video of each user visit like where they clicked and scrolled to make the site a better experience
• Acuity Scheduling \u2013 Nathan uses Acuity to schedule his podcast interviews and appointments
• Host Gator\u2013 The site Nathan uses to buy his domain names and hosting for the cheapest price possible
• Audible\u2013 Nathan uses Audible when he\u2019s driving from Austin to San Antonio (1.5-hour drive) to listen to audio books
• Show Notes provided by Mallard Creatives