Why I Invested in Springboard

Introduction

For the past three years, I’ve been a data science mentor for Springboard: an online bootcamp for software engineers, data roles, and digital designers that gives students a self-paced curriculum and pairs them with mentors in industry. I jumped on the opportunity to invest alongside venture capital firms in Springboard’s Series B round in July 2020. I’m writing this piece to organize and share my thoughts on why I’m excited about Springboard and the self-paced + mentor model of teaching in general. I’ve shared my contact information at the bottom of this post if you’re interested in chatting more about Springboard, the future of learning, or angel investing.

Investment Thesis

The biggest motivator for my investment in Springboard is a frustrating experience that I guarantee all software developers, data scientists, and digital designers have had — probably dozens of times — in their career journeys. One is faced with using a new tool or technique and things just aren’t working or the concepts aren’t clicking. If programming, the results are incorrect, or more likely, you get an unhelpful error message. After an obligatory trip to Stack Overflow or YouTube tutorials, sometimes you still can’t fix your problem. More senior folks can rely on networks either within or outside of their organizations. But what can beginners do? When you’re just starting out and are trying to land that first job in your domain, you certainly don’t have coworkers to rely upon and likely don’t have a strong network of folks at other organizations. If you do, many of them are likely just as new to the space as you are.

The highly motivated and laser focused will spend tens of hours trying to find a solution. The rest of us generally give up with the hope that what they skipped isn’t too important. Understandably, most give up on learning a new domain entirely when they hit enough stumbling blocks in their career journeys. I want to dwell on this for a second, because it’s something I think about a lot. There are millions of people worldwide who are capable of and motivated to pick up these exciting and in demand skills — but due to the current learning ecosystem, and no fault of their own— they’re getting stuck and eventually giving up. It saddens me to think about so much wasted potential because there did not exist effective, reasonably priced solutions to this problem.

Alternatively, there are some folks lucky enough to navigate the stumbling blocks well enough to land that first job. However, many of this group are in for a rude awakening if they’re unable to apply basic techniques or fully grasp core concepts. Generalize this experience across the labor pool and I suspect it explains much of the situation we currently find ourselves in: high software engineer and data analyst/scientist/engineer demand relative to supply. Again, this isn’t because only a select few are capable of picking up these disciplines, but because historically there were only two viable paths into these roles:

Solution: Let students learn as much as possible on their own from a well curated, self-paced curriculum and supplement with regular meetings with a mentor in their area of interest to help them bulldoze learning stumbling blocks.

Springboard logo
Springboard logo

I strongly believe in the value of mentorship because I experienced its benefits myself well before becoming a mentor for Springboard. I was lucky to have mentors—formal and informal—at all steps in my data science journey. I had people willing to chat about how they broke into the space, mentors to help develop technical skills, and ones who gave me advice on data science management and leadership. I strongly believe that without these people I wouldn’t have accomplished what I have so far in my career. Additionally, while mentoring for Springboard, I’ve had countless situations where mentees said things like, “Thank you for explaining that concept! It would have taken me forever to figure that out.” Because of these personal experiences, I wanted to start my investment case for Springboard focusing on a mentor’s ability to eliminate learning stumbling blocks, but there are many other important reasons for my investment I wanted to share as well:

  • Job Guarantee: I’m a major proponent of incentive alignment. By having a job guarantee, and continuing to offer it even through the COVID-19 pandemic, Springboard has unflinchingly aligned its incentives with those of their mentees. If, through their curriculum, mentoring, and career coaching, a mentee is unable to land a job in their domain of interest in 6 months¹, they are eligible for a full refund. The company only succeeds when its mentees achieve their goals of pursuing new careers in their areas of interest.
  • Industry Oriented: From my experience with the data science career track, Springboard does a fantastic job by avoiding a common pitfall that other data science programs fall into: neglecting to teach skills or techniques that are directly applicable to tasks that will be required for data science jobs. Rebecca Vickery’s latest post in Towards Data Science criticizes masters programs in data science for ignoring to teach some important skills, such as Github and how best to communicate takeaways from data. Springboard avoids this by designing their curriculum specifically to help their students land jobs and succeed in them by focusing both on traditional data science curriculum as well as understanding how to translate business problems to data ones, navigate organizational structures, and communicate findings. Mentors, who are currently in industry roles, can make sure both students and the curriculum developers are up to date with latest trends.
  • Time and Cost Efficiency: Springboard is able to let students learn at their own pace. I would have loved this sort of option for college, as I was able to pick up concepts from the assigned reading alone sometimes and therefore found the corresponding lectures a waste of time. On the flip side, I recall being overwhelmed, along with many of my fellow students, while professors whizzed through mathematical proofs that very few if any could follow. By involving mentors only when help or advice is needed, Springboard can save their students significant time and money.
  • Individualized and Contextualized Advice: I’ve seen many beginners to software engineering, data, and design have an overwhelming experience sifting through the mountains of advice on how to get started in these spaces. “Python’s the best language for data scientists.” “R is more accessible and better for a lot of data science tasks like exploratory data analysis.” “Scripting is overrated. All you need is SQL for that first job.” “You need a PhD in math or you won’t get anywhere.” “Requiring a strong math background is overrated and only serves as a gatekeeping mechanism.” Obviously, part of the issue is that not only are there too many opinions to sift through, many of them are conflicting. Mentors who work with a student one on one can not only help them eliminate unequivocally bad advice, but they can also help contextualize conflicting advice. For example, they can explain the math and computer science concepts necessary for succeeding in different types of data roles. A data scientist specializing in visualization needs a very different set of skills than one specializing in machine learning.
  • Network: Springboard also comes with a built-in network of other mentees, alums, and mentors. Those entering their first job search can use this network for leads, advice, and referrals.
  • Accountability: When you’re self-teaching, it’s hard to keep yourself accountable to your goals. Any deadlines that you come up with are hard to enforce. Not only does having a mentor help with accountability, as there’s evidence that sharing your goals with somebody can help you accomplish them, but they also help students set goals that are appropriately challenging, yet also achievable.
  • Success StoryMy Personal Experience: Over the years, my team at Pandora has had five members who were either mentee graduates or mentors for Springboard. The mentees are all highly motivated folks who just needed a little assistance picking up the skills required to succeed in quantitative analytics roles. And mentorship has had its benefits as well, as the mentors sharpened their teaching and coaching skills through the program to better support their teammates and direct reports on the job.

Conclusion

For all the reasons I shared above, I think there’s big potential for Springboard and other high quality self-paced learning + individualized assistance/additional support models, not just for tech sector skills but across the board². I’m excited to see how the space evolves over the coming years, with the hope that these models will both lower costs and improve learning outcomes for students.

Contact

If you’re interested in chatting about taking a course with Springboard or applying to be a mentor or instructor in software engineering, data engineering/analytics/science, machine learning engineering, or UI/UX design, or cyber security, don’t hesitate to contact me via email at jarus [dot] singh [at] gmail [dot] com or a DM on Twitter. My referral can save you up to $1,000 on a course or speed up the application process for a mentor or instructor role. Feel free to also get in touch if you’re interested in chatting about different teaching models or angel investing as well.

Director of Quantitative Analysis @PandoraMusic. Mentor @Springboard. Bridging the gap between business and data teams. Opinions are my own. #rstats