✂️Breaking into Data Science- From Hair Salon to Data Scientist (Ch.1)🔍
Tl:dr 💁️ ➡️ 👩🔬;
How I went from hair salon girl to data scientist working for an innovative digital health company with no Master’s, PhD, or background in a quantitative college degree.
“Work is love made visible.” — Kahlil Gibran
“The written word is all that stands between memory and oblivion. Without books as our anchors, we are cast adrift, neither teaching nor learning.
They are windows on the past, mirrors on the present, and prisms reflecting all possible futures. Books are lighthouse erected in the dark sea of time.” — Disney’s Gargoyles, Season 2
My goals are to provide a comprehensive overview of my data science journey, answer common questions about becoming a data scientist, and encourage aspiring data scientists of all backgrounds.
I am incredibly happy and passionate about working in data science. Title of data scientist aside, I feel incredibly fortunate to work in a field that is constantly innovating, on projects that have immense strategic impact, and teammates and colleagues that are ‘Wicked Smaht’.
The reality is that there’s ton of misconceptions about what it takes to become a Data Scientist (along with the existential ‘What IS a data scientist?’). And with every data science & machine learning influencer spouting their opinion of what a “real” data scientist is (unicorn, amazing full-stack engineer capable of whipping out proofs on a whiteboard & regurgitating Cracking the Coding Interview cover to cover)…it’s easy to feel like there’s an ivory wall meant to keep the rest of us non-savants out.
It’s almost as if everyone’s in such a rush to validate their status that the actual requirements are arbitrary.
Data science can be this incredibly interesting, beautiful and creative field that can also seem impossible to break into. I understand how frustrating it can be to read through all these blog posts as an aspiring data scientist and still wonder “Were you technical to begin with?”, “What was your background?”, “How did you fit studying in?”, “What did the rest of your schedule look like?” and even “Does your advice and story even apply to mine?”.
My hope is to provide insights gained from my own experience breaking into data science.
In short, my motivating factors for writing this series include:
- A desire to contribute my perspective to the numerous diverse stories on becoming a data scientist;
- Providing aspiring data scientists with the necessary tools to navigate upskilling and the job search;
- Document for easy distribution;
- Showing appreciation for the people & places that were instrumental to my career development.
Successful careers are rarely accidental.
More often they start with a spark of inspiration, are pushed forward through a series of fortuitous events with grit & elbow grease, and powered by faith.
The most important factor though is inevitably the kindness of strangers (and not so strangers) who are rooting for you all the way through.
This is my thank you note to everyone who made it possible for me to find a career that I love so much.
Love you and thank you, to my family, friends, mentors and the other part of my soul.
Chapter 1: Pre-Data Science
Not Quite Med School Material
“Progress is born of doubt and inquiry.” — Robert G. Ingersoll
“If you’re not prepared to be wrong, you’ll never come up with anything original.” — Ken Robinson
Like many kids of Tiger parents, I’ve had the great honor of disappointing my family twice: the first time, when I didn’t get into Harvard or Stanford; and the second time, when I chose to not pursue medicine.
High school could be best described as a “grind”, four years of sleeplessness and burning the midnight oil to keep up with the overachievers. In keeping with the nerd crowd, my high school experience could be quantified by over 13 AP Exams (10 in my senior year alone), 3+ student leadership titles, scholarships for writing and journalism, and a pile of college applications stamped with SAT scores. I had no aspirations or inclination to pursue data science, machine learning or any of their precursor disciplines in math or computer science, having been told for years that I wasn’t really “that smart of a person”.
After receiving acceptance letters to some great schools that were not Harvard or Stanford (namely Berkeley, Cal Poly, UC San Diego, etc) I chose to enroll in UC San Diego’s biomedical engineering pre-med track. I was determined to go into medicine to help people at scale and then spent the next 4+ years becoming disillusioned as I realized how much of medicine didn’t, in fact, scale and biomedical engineering was far more research and mechanical engineering focused.
I jumped from major to major and settled on economics and anthropology as my final choices in my 4th year of college. I’d developed a deep love and appreciation for the study of humankind and spent my senior year diving into game theory, behavioral economics, decision making, evolutionary psychology and public health policy.
Another six years would pass before any of the coursework would be even remotely applicable to my line of work. As I pointed out to one of my Data Science Dream Job students recently (who was saying that their IT-only background precluded them from a career as a data scientist),
The first job I got out of college? Hint, I talk about it in the screenshot above.
You guessed it, it was ‘Unemployed’.
The reality was that when I graduated in 2013 I had no marketable skills, including technical skills like programming (nadda, zilch).
I could do some basic analysis with R from taking one biostatistics classes and some basic statistics classes (as well as multivariate calculus and linear algebra) but only what was required for my major’s prerequisites (and solving the homework problems).
Searching for a job for four months was soul sucking.
Months of no responses lead to depression and I lost 20lbs from not eating (finally! The size 6 pants I always wanted!). I’d run 7 miles a day from my parents’ apartment to the Golden Gate Bridge because I couldn’t stand the failure I saw in the mirror and literally running from my problems seemed the healthiest option. At the end of my run I’d sit by the piers, look out across the fog to the lighthouse ringing forlornly and wonder if my life had been a complete waste.
Did I really have anything to look forward to?
Against that backdrop, “Data Science”, “Machine Learning”, “Big Data” or even “Advanced Analytics” weren’t terms that were even on my radar. My goal as a newly minted graduate with almost no real marketable skills was just to survive and enter the job market (forget meeting the prerequisites for an entry-level data scientist job).
On the fourth month of Craigslist applying I received an email from a small hair salon owner asking if I would come in for an interview. The pay was below the poverty line in San Francisco and I would struggle.
A job was a job though and I was ecstatic.
At that point many of my friends were well into their first jobs or even working for brand name tech or finance companies. The gap between where I was and where they were would keep coming up in a number of ways, like not being able to afford the same expensive places they liked to go to, not being able to go on trips, feeling the pinch of the drinks bill.
I began to experience intense shame that I wasn’t where everyone around me was in terms of money, professional development, and socializing.
My lifelong mentor and his wife would regularly invite me over to their place with tea and cheer me up. Occasionally Alfred would tell me that one day I would miss this period of my life and that the lessons I learned would be incredibly valuable. The visits and advice kept me sane and going and I’m incredibly lucky to have had their support in that regard for the past 9+ years (as he had also been my fencing coach in high school).
Even though “hair salon girl” isn’t a title most people would brag about their LinkedIn profiles or resumes, I learned some of my most important lessons from opening the shop in the morning and sweeping customers’ hair trimmings. Lessons that included humility (the ability to see the human underneath the job) and ownership (the ability to take pride and pursue excellence regardless of the job).
In between sweeping hair and changing out shampoo bottles I’d talk to our clients (many of whom worked for start-ups) and learn about the new careers starting to pop-up in the tech industry. Over time I started to ask myself, “Why can’t I be working on the kinds of projects they do?”
“What separates me from them?”
After experiencing another verbally abusive client I was determined to go into the tech industry and prove myself.
Seven months later I quit the salon and landed my first role as a sales hacker for an early stage recruitment tech startup. I had no clue what I was doing (and from an experience standpoint, almost nothing to offer other than grit and energy) and they really shouldn’t have hired me, I was so ridiculously unqualified to be successful in that role. At the coffee interview in SOMA’s Sightglass (of course) I asked honestly why they would take a chance on me for the role. My manager Jenn said that I had the right attitude, sounded smart, and showed drive to learn what I didn’t know.
I’d always been a book worm but never considered business or personal development books as anything but “self-help” books.
However, I knew I needed help in my new endeavor from my mentors at a distance and understood that I didn’t have all the fundamental tools for excelling professionally, financially, and emotionally.
During that time I read books that would eventually become incredibly influential for my career, including:
- Cal Newport’s “So Good They Can’t Ignore You: Why Skills Trump Passion in the Quest for Work You Love”,
- Reid Hoffman’s “The Start-Up of You”,
- Meg Jay’s “The Defining Decade: Why Your Twenties Matter And How to Make the Most of Them Now”,
- and Mihaly Csikszentmihalyi’s “Flow: The Psychology of Optimal Experience”.
My mentors at a distance helped me learn that:
● Career advancement is about the strategic accumulation and leverage of career capital;
● Careers can be directed and should be a combination of taking informed risk (in the form of opportunities) and through developing a craftsmanship mindset;
● Much like startups your goal in your career should be innovate, test, gather feedback and re-adjust;
● Highly desirable careers are characterized by: Autonomy, Competence, Relatedness;
● Highly desirable careers are attained through high skill & experience (aka you got to work at it — nothing is handed to you);
● People can change & change in outcome comes from change in actions.
Realizing that I was the only person responsible for MY career and professional fulfillment was freeing.
With this knowledge, I was able to take a step back from the mess of my college career (that was ultimately capped by a generous overall 2.3 GPA) and introspect (as well as reframe my college narrative). The intense shame of my performance in college and inability to be successfully passionate was unconsciously plaguing me and holding me back from taking purposeful control of my destiny.
In high school all my motivation for the pre-med route had been external (i.e. get good grades to avoid getting yelled at, sign-up for extra classes to get into an acceptable college so I didn’t get yelled at and embarass the family, etc).
When I got to college and no longer had the external pressure of parents and report cards, the scaffolding really began to fall apart as I had no internal direction or North Star.
As soon as I learned that getting a great career was less like bass fishing and more like building blocks, I began to really start planning out and thinking critically about the ROI of my most precious (and only) asset, since I was flat broke and living with my parents: time.
The first couple months of working at my first start-up RecruitLoop comprised of going out salsa dancing and drinking 3–4 days a week with my start-up friends (really productive stuff, huh?).
One Sunday while getting brunch-time pina coladas, one of my friends bemoaned how she was surrounded by curious and industrious geniuses at Google and couldn’t understand how they also maintained side hustles and personal projects. Her boyfriend responded with “they’re probably not having pina coladas on a Sunday morning” and it hit me that I was wasting valuable time that could be spent improving my mind, body and soul. (That ended pina-colada sundays for me.)
With help from my bodybuilding friends from the gym, I broke out of the drinking and partying circuit and built up a routine where I’d spend my days working and my nights and weekends taking both online and community college classes in Java, database development and GIS.
Afternoons or nights would be spent working out with my gym buddies.
I’d also learn as much as possible from my leadership and ask them for suggestions on books to read (Mike’s recommendation of The Minto Principle has served me incredibly well, along with Jenn’s growth book recommendations), upskilling resources, and conference tickets.
(Note: I am incredibly grateful to my mentors Jenn Steele, Michael Overell, and Paul Slezak as they helped me learn the guts of sales operations and the recruitment industry during my time at RecruitLoop. Between setting up the lead gen funnel, learning the ins and outs of Salesforce and other key sales enablement tools and sales analytics, I learned so much and they’ve continued to support my career beyond RecruitLoop).
Over the next couple years I would pivot from being a part of growth ops at RecruitLoop, to performing data analysis and report automation at Digimarc (an anti-piracy company), to strategic FP&A sales modeling at Sunrun (the nation’s leading residential solar company).
A Non-Lateral Move
“If everyone is thinking alike, then somebody isn’t thinking.” — George S. Patton
“Millions saw the apple fall, but Newton asked ‘Why’.” — Bernard Baruch
By the time I reached Sunrun, I’d managed to come across a number of data scientists and data science meetups. I was captivated by the kinds of complex and challenging problems they were solving (and sometimes creating). I was also beginning to focus away from strategy and towards more data-driven projects.
People ask me: “Was there some stroke of inspiration?” “Was there a show or movie like ‘A Beautiful Mind’?” “ Did I get my hands on a dataset or python tutorial and feel the bite of passion?”
The answer is: Nope.
I’m one of the most risk-adverse obsessive-planner types out there.
The pivot to data science was a strategic decision in that:
● I didn’t want a senior role in sales ops/enablement
● I didn’t want to continue in finance
● I had built up marketable skills in data
● I knew that in order to become a leader in data science (or analytics) I needed to develop a breadth and depth of valuable experience in big data and predictive modeling — and I especially wanted to be a “player-coach” leader, as opposed to a purely administrative manager.
Around that same time I received a call from my mentor and prior manager Eraj Siddiqui about a potential opportunity.
We had worked together at Digimarc and he had moved on to an innovative company called Autodesk and was hiring for a hybrid data analyst/data scientist role focused on customer success and product adoption.
After receiving an offer I threw myself into the work and projects.
I learned so much about product adoption, analyzing feature usage, segmenting users, tracking customer success KPI’s and preparing for QBRs and am incredibly grateful to Eraj Siddiqui, Jeremy Robson, and the BIM Adoption team for the growth experience.
By this point in my career I had accumulated domain experience working with every core business partner possible (finance, sales, marketing, customer success), developing both a deep and wide understanding of key business metrics & reporting tasks, as well as an understanding of the “funnel”.
I’d also experienced working with great companies in a variety of industries like solar, anti-piracy, recruitment tech, and modeling software. I now had some skills in SQL and R (including creating an automated scoring system that would load & summarize usage data, apply rules-based logic to determine the trend of usage, load labels into Salesforce via API and R packages, and then send email notifications to adoption reps summarizing the accounts needing follow-up).
However when I started working on more complicated questions like “Predicting user churn”, “Automated segmentation & analysis (aka clustering)”, and “Predictive health scoring” I began to struggle as the very junior “data scientist on an island”, including accessing, processing and visualizing big data for machine learning.
I realized I didn’t have quite the tools, skills or resources to learn and develop into the kind of data scientist I wanted to be and this would become very evident. (To Be Continued in Chapter 2)
Thanks for reading this far!
If you’re interested in learning more about my experience with the data science job search process, I write about attending a boot camp and upskilling in Chapter 2.
If you’re interested in learning more about MLOps, production ML, & distributed systems + cloud development, I also publish:
- Videos on Youtube at The MLOps Engineer
- Written Snippets on Substack
- Snark & saltiness on Twitter
- Professional sounding/career oriented stuff on LinkedIn
- Code & TODOs on Github
And I take support & patronage in the form of coffees ☕!
Interested in continuing to learn more about my journey to Data Science? Please check out the next following chapters (soon to be published):
- [You Are Here✔️ ➡️] Chapter 1: Pre-Data Science(aka “Why I Became a Data Scientist”) — This section covers my background and my early professional career. Key Takeaway: It’s not about the degree! 🎓
- Chapter 2: The Uphill Climb to Upskill (aka “How I Became a Data Scientist — sort of…”) — This section covers my experience upskilling and attending a boot camp. Key Takeaway: Commit to investing in you! ✏️
- Chapter 3: The Hunt Begins (aka “How I Got a Job as a Data Scientist”) — This section covers my experience job hunting, from applications to the onsite interview. Key Takeaway: Swing for the fences and dig deep! 🏏
- Chapter 4: The End is Just the Beginning + FAQs (aka “The Offer and Introspection”) — This section covers my offer and summarizes the high and low points of journey to “becoming” a data scientist and next steps. I’ve also expanded this chapter to include the top questions I get about becoming a data scientist. Key Takeaway: The End is just the beginning! 🎉
