How Mikiko Became Head of AI Developer Relations After Springboard

Mikiko Bazeley searched for a data science bootcamp that would allow her to continue working while she leveled up her career – that search led her to Springboard! Find out how Springboard’s curriculum, flexible scheduling, meaningful mentorship, and affordability gave Mikiko the headstart she needed to pivot from data analytics into data science. Plus, learn how Mikiko has continued to develop her skills to become the Head of AI Developer Relations at an artificial intelligence company!

What were you up to before starting your career in data, Mikiko? 

My mom immigrated to the U.S. from Japan when she was in her early twenties without a college degree, which wasn’t unusual at the time. As a second-generation Japanese American (nissei), college education was emphasized as being a ticket to incredible success and to move up and beyond being middle class in San Francisco. 

There’s a lot of pressure on immigrant kids to become either a doctor, lawyer, or engineer. I was inspired by Doctors Without Borders and the work they were doing, so I got into the biomedical engineering program at UC San Diego on the pre-med track. Even though I was ambitious in high school, I realized I was underprepared for college and I needed to figure out a major that would allow me to simply graduate college. I sailed on a combination of anthropology and economics because I was interested in the dynamics of how power influences behavior. I managed to graduate with a 2.6 GPA, but I graduated college without any marketable skills!

What inspired you to specialize in Data Science after graduating from college with a degree in economics?

After college, I found a job at an expensive salon where tech folks happened to frequent. I realized that they weren’t remarkably special, so I pursued working at a startup called Recruitloop. They were looking for someone young and hungry to get into sales ops, so I got hired to do Salesforce admin and marketing operations, along with initial data scraping. After that I worked at an anti-piracy company, which started pushing me towards the data analyst route. When I arrived at Autodesk as a hybrid data analyst, I learned that I could go down a managerial path, I could work in finance, or I could go into data science.

There are so many Data Science Bootcamps out there — what set Springboard’s Data Science Career Track apart from other programs?

My GPA came back to haunt me when I realized I had killed any prospects of the traditional academic path to career growth. I considered other in-person data bootcamps, but they were expensive and required quitting my job. I didn’t have the means to take out a big loan and I couldn’t ask my family for help. If I quit my job I’d also lose 3-6 months of relevant job experience! 

I came across the Springboard Data Science Career Track and it checked a lot of boxes for me. I chose Springboard because:

• It was virtual, remote, and self-paced so I didn't have to quit my job and there was flexibility in scheduling. 
• It was part-time. At that time, I ended up doing the bootcamp for 9 months, but typically students finish it in 6 months. 
• The tuition cost was reasonable (even with the extra 3 months I took). 

I was also able to take the projects I was working on in the bootcamp, and the techniques and skills I learned, and directly apply them to my job as a data analyst!

Mentorship is a key piece of the Springboard program — what was your mentor like?

I loved my mentor, Rajiv Shah -- he is doing amazing things now! Rajiv would look over my projects and do the code review on the assignments, but at every single session, I would bring him questions or challenges that I was running into in my data job, trying to educate my key stakeholders and peers on why we should be doing more data science modeling and predictive work as part of the analytics team. He helped me refine talking points and navigate situations, which I didn't realize would be so important later on in my career where I'm constantly having to sell why we should follow best practices or consider bringing in certain tools. 

At this point in your tech career, was Springboard worth it for you? 

Overall, it was a fantastic experience and Springboard really helped me pivot to data science because after that I was able to get a job as a data scientist working on growth for a company. If I hadn’t done the program with its structure, I would not have successfully pivoted into data science for a number of years. I definitely wouldn't have had the ability to progress both in machine learning engineering and eventually into leading developer relations.

Has your Springboard experience continued to help you throughout your data career?

Having that domain experience combined with the skills I acquired in the Springboard data science bootcamp helped me secure my first real data scientist job! 

Springboard taught me Python and I've developed in Python ever since. A lot of the data science/machine learning 101 concepts I learned at Springboard (like data quality and feature engineering) are still very relevant to my current role!

You’ve had quite the data career since graduating from Springboard — you’ve gone from Data Analyst to Data Scientist to ML Ops to now Head of AI Developer Relations at an AI company! Over the course of your career, have employers been interested in your Springboard bootcamp experience?

There is a bias towards PhDs and master's degrees in the data science/machine learning world, but most hiring managers can read between the lines on a resume that doesn’t have a PhD. Employers cared more about what I took out of the Springboard bootcamp experience and how I combined it with my existing domain experience and how I would continue to build on that. 

In your opinion, do you need previous experience in data analyst or data scientist roles in order to land these new artificial intelligence roles?

Not at all. The tools have been so democratized at this point that anyone can go build machine learning projects and products. There's a new kind of tech role called the AI Engineer or AI Developer, which is basically a software engineer that is able to use a lot of the machine learning libraries and tools to build a product. They don't necessarily need to go through that whole data-to-machine learning cycle. 

That said, it can be really hard to build a road map for yourself to upskill on your own if you don't know what you don't know. If you don't have a way to build a road map of skills for yourself or you don't have help from someone to build that road map, then I think it can be pretty challenging navigating what's hype and what’s real, in terms of the current AI tooling landscape. 

Generative AI is rapidly evolving. How have you stayed ahead of the curve?

My three most important practices and habits are:

1. Write and talk about AI. I do a lot of writing and diagramming. The best way to understand something is to teach it, so that's one way I hammer in the fundamental principles.
2. Build projects. Even if that means building an MVP in a Jupyter or Colab Notebook, it shows that you’re able to build the tools and then understand what the overarching patterns are. Show that you know the principles behind the design of an API or a library. Once you pick up certain patterns, it becomes easier to navigate what's being pushed out. 
3. Know your own interests. I wanted to use my tools to be able to solve problems and understand the world and be able to build interesting products or projects using machine learning. Knowing that that's my aim, I can filter down to what matters most to me.

Let’s talk about your current role at the data-centric AI and intelligent application platform, Labelbox. What does a Head of AI Developer Relations do? 

Developer Relations (also known as DevRel) is an umbrella term covering the “strategies and tactics for building and nurturing a community of mutually beneficial relationships between organizations and developers as the primary users and often influencers on purchase of a product.” The tl;dr: My goal through the developer relations program is to nurture the next generation of builders and champions by delivering an exceptional developer experience. 

The three main pillars are: 

1. Developer education in the form of content events.
2. Building a developer community by aligning all the different individuals that are involved in building AI (including data scientists, ML engineers, ML Ops engineers, data engineers, platform engineers, AI engineers) and making sure we help build and curate these third spaces. 
3. Enablement with regards to the product, like helping to contribute to the docs and give feedback. A huge part of this is helping to inform the business about what's going on in the AI builder community and sharing what I'm hearing from the field. 

For example, I’m asking questions like: Do we have docs? Do we have a GitHub repository with example projects? Do we have videos that people can watch if they're trying to figure out how they get from raw and structured data (like images, text, audio, or visual) to building a fully fledged ML product in as few steps as possible with as little friction as possible?

Typically with consumer products, you don't have to think about that, but for a heavily technical product like Labelbox, we do and it’s a huge part of my role. 

Some weeks I focus a lot more on content, like this week I'm finishing up writing some technical blog posts. I also have to work on an MLOps keynote for a community conference called Data Day Texas, helping to level-set people what they should expect in 2024 in MLOps. I talk to data scientists, ML engineers, and data engineers on various kinds of social platforms to understand what's resonating with them. When I put out content, I ask stakeholders what they like and hate about it. I help with code examples, and in the next few weeks, I'll be more focused on community, building out events and a course around how to use Labelbox with other tools.

You're in a senior data role now —What is the next rung on the data/AI career ladder for you? 

In terms of a seniority dimension, I'm in a good spot. I can eventually build out a team below me and then focus on program management. Right now, my job is a combination of program management and individual contributor work. I’ll be able to stay at this level and continue to grow as the industry continues to evolve. A lot of what I do is help simplify all the magic that's going into the world and make sure that our technical team understands who our users are, how our users think, and how we align the business in a way that we're delivering the most value to our technical users.

What advice do you have for current Springboard students who are interested in an AI career? 

1. You get what you give. You truly have to show up for the work, do the assignments, and keep an eye on how to incorporate new tools into your projects. You can’t just clock in and expect a job offer to land in your lap. That’s not how the industry works, regardless of what bootcamp you choose.
2. Invest in your personal branding. The loudest ones on social media are the ones amplifying their brand. Show off your work and what you’re interested in to future proof your career. Showcase what you learn and how you package and productize it into something valuable to the people around you going down the same path. 
3. Never stop building. There is no negative outcome to building personal projects and to continue learning. Springboard walks you through how to build a project. 

I’ve seen a lot of companies restructure their data science teams because they’re too focused on research and development and not enough on business value. Data professionals that can learn to quickly prototype a project and focus on features that will help drive business revenue are the ones that will survive layoffs. 

Find out more and read Springboard reviews on Course Report. This article was produced by the Course Report team in partnership with Springboard.