Side Hustles for Data Scientists

Episode Deep Dive

Guest introduction and background

Keith McCormick is a data scientist with over 25 years of experience, known for his work on SPSS Modeler and his numerous consulting and training engagements. His specialty is guiding data science teams and helping organizations manage analytics projects effectively, rather than just building the models himself. Beyond corporate consulting, Keith has worn many hats as an entrepreneur, speaker, author, and trainer, all of which have helped him build a flexible lifestyle and establish a strong reputation in the data community.

What to Know If You're New to Python

Here are some short tips to help you get the most out of this conversation if you’re newer to Python and data science:

• Understand that Python is both a language and an ecosystem: It’s powerful for data analytics, modeling, and building websites or APIs.
• Familiarize yourself with basic Python data science tools like Jupyter, pandas, and scikit-learn.
• Appreciate that Python’s open source nature can open doors for side projects, freelancing, and building new skills.
• If you need a starter resource, check out Python for Absolute Beginners: It covers the fundamentals to help you quickly level up.

Key points and takeaways

1. The Power of Side Hustles for Data Scientists Building a small-scale project or gig on the side can be a lower-risk way to explore new technology or business ideas before committing full-time. It enables data scientists to experiment with new libraries, gain practical experience, and generate extra income or networking opportunities. Side hustles come in many forms—from small consulting retainers, training workshops, or building open source libraries that might evolve into a service. This layered approach can also serve as a credibility boost, showing others you have real-world, entrepreneurial experience.
   • Tools and links:
     • Kaggle.com for data competitions
     • GitHub.com for publishing and showcasing projects
2. Retainer-Based Consulting vs. Hourly Gigs Instead of working one massive client project 40 hours a week, Keith recommends having multiple retainer clients. A retainer model spreads income risk across several projects and offers flexibility to travel, attend conferences, and work on diverse assignments. Retainers also help avoid the feast-or-famine cycle common in consulting, so you always have something in the pipeline.
   • Tools and links:
     • Upwork.com for initial freelance gigs
     • Codementor.io for short-term, on-demand mentoring
3. Conference Speaking and In-Person Training Speaking at conferences and teaching workshops can raise your profile significantly. Even if it's a single-hour talk or a one-day pre-conference tutorial, you gain direct exposure to potential clients and collaborators. For data scientists, presenting niche topics—like how you use Python in real business scenarios—can lead to more consulting deals and often gets your travel and conference ticket sponsored.
   • Tools and links:
     • PyData.org for data-focused conferences
     • Local Python user groups and meetups
4. Leveraging Webinars and Online Training Hosting or guest-presenting on webinars is an excellent, lower-barrier way to build your reputation. Even if the gig is unpaid, you get in front of an audience without travel, and you often can feature your specialized data science or Python knowledge. Platforms like LinkedIn Live, YouTube Live, or a vendor’s official webinar series can help you reach hundreds or thousands of viewers from your home or office.
   • Tools and links:
     • YouTube.com for live or recorded presentations
     • LinkedIn (search for official “evangelism teams” or “developer advocacy” groups for data science companies)
5. Book Writing and Technical Reviewing Authoring or co-authoring a technical book can be a highly effective credibility builder, though the royalties alone may not be lucrative. Equally powerful is becoming a technical reviewer for someone else’s book, which lets you influence published materials, deepen your expertise, and forge industry connections—especially with authors or publishers who can open other doors.
   • Tools and links:
     • PacktPub.com (frequent tech publisher)
     • LinkedIn for finding authors seeking reviewers
6. Teaching at Universities or Community Colleges Adjunct teaching is an excellent mid-career option for data scientists looking to share real-world expertise. While it may require at least a few years of experience, it can solidify your reputation, expand your network, and give you a structured environment to refine your teaching skills. Colleges increasingly need instructors who can teach Python-based data science courses, making this a relevant and in-demand opportunity.
   • Tools and links:
     • University extension programs (e.g., UC Irvine, local state universities)
     • Online portals where institutions post adjunct openings
7. Building an Open Source Project and a Business Around It Many successful data-centric companies start with a popular open source tool, then offer specialized services or commercial hosting layers (like Scrapinghub for Scrapy or Explosion AI for spaCy). If you have a promising library or framework, providing consulting or advanced features can transform your project from a passion into a real revenue stream. This approach leverages both Python’s open source ecosystem and your unique position as the project creator.
   • Tools and links:
     • Scrapy.org and Scrapinghub.com as an example
     • spaCy.io and Explosion.ai
8. Kaggle Competitions and Recognition Ranking in the top percentiles—or even just getting a decent position—on Kaggle can lead to consulting, advisory board invitations, or job offers. Even if you don’t win the big prize, demonstrating advanced knowledge in a public, competitive environment sets you apart. Companies sometimes use Kaggle results as a talent discovery method.
   • Tools and links:
     • Kaggle.com for competitions and community forums
9. Moving Beyond Excel with Python Many companies live in Excel, but exploring Python data tools can lead to new opportunities and side work—especially if you can show businesses how to automate or scale beyond spreadsheet limitations. This specialized knowledge creates immediate value, whether you’re mentoring coworkers, building custom scripts, or migrating entire organizations to Python-based workflows.
   • Tools and links:
     • Move from Excel to Python with Pandas Course
10. The “Rocket Launch” Analogy for a Side Hustle Balancing your day job with a new side project feels like that rocket pushing through max aerodynamic pressure—things get harder before they get easier. Once you break through that early phase of intense juggling, you can power down your main job or scale back other responsibilities if your side hustle takes off. It’s a practical metaphor for avoiding the risk of instantly quitting a job and seeing whether your idea proves itself first.

Interesting quotes and stories

Keith mentioned how he “did 13 five-day training weeks in a row” in the early days—revealing the extreme hustle he embraced to grow his experience and income.

The discussion about how “a single book or open-source project can establish instant credibility” highlights the importance of picking the right project at the right time.

The rocket launch analogy for side hustles was also compelling: you need to survive that “max Q” period when balancing multiple priorities before your project (and career) can skyrocket.

Key definitions and terms

• Retainer Model: A consulting approach where a client pays a recurring fee for a fixed set of services or time every month, rather than working only on a project-by-project basis.
• Technical Reviewer: Someone who reviews in-progress manuscripts or code examples for accuracy and completeness before publication.
• Transparent Reactive Programming: A programming approach (like Shiny in R) where the framework automatically updates outputs when the underlying data changes, without manually writing many callbacks.
• Kaggle Grandmasters: Competitors who have consistently ranked in the top tier of Kaggle competitions, often hired as advisors or data science leads.

Learning resources

Here are additional ways to deepen your skills and knowledge based on the episode themes:

• Python for Absolute Beginners: Ideal for getting foundational Python knowledge if you’re new to coding.
• Python for Entrepreneurs: Great if you're serious about turning your side hustle into an online business and need step-by-step guidance.
• Move from Excel to Python with Pandas: Perfect if you or your workplace rely heavily on Excel and you want to automate and scale with Python.

Overall takeaway

Side hustles open the door to a more flexible, creative, and opportunity-rich career in data science. By gradually building projects, speaking at conferences, writing, or even founding an open source project, you can test new ideas without leaving your day job. It’s a practical and empowering way to develop skills, build a reputation, and diversify your income—and might be exactly the catalyst you need to launch yourself into the next phase of your data career.