In Episode #722, Nathan interviews Michael Segala. He’s the CEO and co-founder of a company called SFL Scientific, a data science consulting firm that specializes in big data solutions. He’s for leveraging machine learning in analytics techniques to arrive at insights to numerous industries— 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? – The Challenger Sale
  • What CEO do you follow? – Larry Page and Sergey Brin
  • Favorite online tool? — Slack
  • How many hours of sleep do you get?— 6
  • If you could let your 20-year old self, know one thing, what would it be? – “Diversify my education, learn more than just science from the early set, it will help you out”

Time Stamped Show Notes:

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

3 Key Points:

  1. Consider yourself lucky when you’re completely bootstrapped and you end up getting your first client only after a few weeks of launching.
  2. It’s 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 – The site Nathan uses to schedule emails to be sent later, set reminders in inbox, track opens, and follow-up with email sequences
  • GetLatka – Database of all B2B SaaS companies who have been on my show including their revenue, CAC, churn, ARPU and more
  • Klipfolio – Track your business performance across all departments for FREE
  • Hotjar – Nathan uses Hotjar to track what you’re 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 – Nathan uses Acuity to schedule his podcast interviews and appointments
  • Host Gator– The site Nathan uses to buy his domain names and hosting for the cheapest price possible
  • Audible– Nathan uses Audible when he’s driving from Austin to San Antonio (1.5-hour drive) to listen to audio books
  • Show Notes provided by Mallard Creatives

The post This Machine Learning Agency did $800k Last Year appeared first on Nathan Latka.

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