5 years, I left Brex in August. Time actually flies. I joined Brex when it was three years previous, and I can nonetheless vividly bear in mind my first day. On the identical time, these 5 years felt for much longer. That is all as a result of in depth information I’ve gained alongside the way in which, from becoming a member of a younger knowledge group and studying the complete knowledge cycle, to collaborating with Operations, GTM, and Product, and finally transitioning into administration. This accelerated development is among the greatest advantages of becoming a member of a startup as an information scientist.
“Shall I be part of a startup or a extra established firm?” I hear this query from my community on a regular basis. On this article, I’ll share the information scientist’s life at a startup primarily based by myself expertise. Hopefully, this can show you how to navigate by way of your personal profession path.
A fast be aware: “startup” is a broad time period that covers many sorts of corporations. For context, Brex is a US-based fintech startup that gives company playing cards, banking providers, and expense administration software program for enterprise prospects. I joined Brex after three years of full-time expertise in product knowledge science. On the time, Brex was a three-year-old Collection C startup with 500+ workers. Due to this fact, my expertise could differ from what you’d expertise at an earlier-stage firm.
I. Area-specific vs. Objective-oriented
Startups transfer quick — they should alter methods primarily based in the marketplace suggestions and iterate consistently to make sure product-market match. Information Scientists, as an costly useful resource, must shift their focus space accordingly to create the most important enterprise worth.
Taking my very own expertise for instance:
- After I first joined Brex, the precedence was to onboard as many shoppers as we may whereas controlling for fraud threat. Due to this fact, I labored intently with the Onboarding group to optimize the account utility circulate and enhance useful resource allocation for the Operations group.
- As our buyer base grew, the following precedence was to scale our buyer help (CX) operate to offer the best-in-class help. I partnered with the CX group to investigate buyer ache factors and cut back product frictions. (Learn my article for extra particulars on DS in CX.)
- Later, I collaborated with the Implementation group to speed up ramp-up for brand spanking new prospects and with the product group to establish churn drivers and enhance retention.
As you may see, startups are very goal-oriented. At giant corporations, you may concentrate on one product function for years; At a startup, the corporate’s wants can utterly change inside 1 / 4, and knowledge scientists change their domains extra steadily consequently.
The upside is that this permits the information scientists to discover and collaborate with extra features and have a extra holistic view of the enterprise. I labored with practically each group at Brex up to now 5 years — watching how GTM (go-to-market) discovered prospects and closed offers, seeing how Product and Buyer Help labored collectively to deal with buyer ache factors, and studying how the Buyer Success group lowered churn and drove upsell. It is a far simpler approach to perceive enterprise operations than studying books or taking programs at a enterprise faculty. It additionally ensures the information scientists have clear and high-impact enterprise questions to unravel.
However what’s the problem? Information scientists must comply with the corporate’s technique and adapt to adjustments consistently. This implies shifting priorities accordingly, and generally having to construct issues the quickest approach, as a substitute of probably the most scalable or dependable approach. Tech debt piles up shortly with tons of one-off dashboards and a number of variations of the identical metric, complicated stakeholders and the information group itself. Establishing the very best practices takes time, so residing with the chaos generally is unavoidable — although with the ability to arrange the requirements from 0 to 1 (or seeing others doing so) can also be a rewarding a part of working at a startup, particularly for aspiring knowledge science leaders.
II. Information Analyst vs. Information Engineer vs. Information Scientist? All of the Above.
Job titles in knowledge as of late are complicated — Some Information Scientists give attention to experimentation, whereas some are deep in machine studying; Some Information Analysts merely construct dashboards, however some primarily do product analytics. However on the subject of startups, titles matter much less — you’ll possible do every thing.
After I joined Brex, everybody had the identical title, “Information Scientist”, however all of us needed to put on a number of hats. We break up the information group into DS, DA, and DE features solely since early final 12 months. As I discussed above, startups are goal-oriented. Due to this fact, when there are restricted individuals and no clear group construction, you’ll need to do every thing to realize the objective.
- Information Engineering: I realized knowledge modeling, Airflow, ETL processes, SQL optimization, and lots of different knowledge engineering expertise up to now 5 years. I bear in mind considered one of my first tasks at Brex was emigrate our knowledge pipelines from dbt to an inside instrument. Sure, as an information scientist at a startup, there’s a excessive probability you additionally must construct, personal, and preserve your personal knowledge pipelines.
- Analytics: Becoming a member of a startup means there are lots of enterprise areas with no or minimal knowledge help. Due to this fact, to assist the group higher perceive their efficiency and achieve their belief, the very first step (after you may have constructed the information pipelines) is to outline the success metrics and construct the dashboards. As soon as the metrics are intently monitored, questions like why they moved or methods to transfer them come subsequent naturally. These are all frequent analytics duties.
- Information Science: There are additionally loads of superior knowledge science use circumstances at startups. Machine studying fashions are useful to detect fraud, predict churn, estimate LTV (lifetime worth), and so on. Causal inference can also be crucial to guage the impression of a advertising marketing campaign or a product launch.
Is it or unhealthy factor to put on a number of hats?
In case you are not but positive about which sort of information observe you’re most focused on, becoming a member of a startup will show you how to to get some taste of every thing and resolve which path to go subsequent. Or if you want to turn into a Head of Information and even construct your personal firm sometime, this full-cycle publicity is tremendous worthwhile.
In the meantime, the draw back can also be very clear — you’ll spend time on issues you aren’t focused on, or not that related to your long-term profession objective. The truth that you personal the entire knowledge lifecycle may also confuse stakeholders, as they often solely care a few sure sort of output, for instance, dashboards or fashions, however don’t notice it’s worthwhile to spend one other 50% of time on knowledge pipelining.
III. Larger visibility
Information scientists at startups by no means lack visibility. From day one at Brex, I started working instantly with the C-suite. Management will usually come to you with pressing and necessary enterprise questions, hoping you are able to do some knowledge magic to uncover insights and drive enterprise development. This isn’t one thing you often get to expertise at a longtime firm, particularly as a junior knowledge scientist. It’s a high-pressure however extremely rewarding atmosphere.
For instance, throughout the Silicon Valley Financial institution Disaster in March 2023, many startups had been impacted, going through the chance of shedding their operational funds. I labored very intently with the management group to assist startups survive this difficult time. I created a real-time tracker on new buyer purposes, analyzed utility overview velocity to estimate further workforce wants, and collaborated with different DS to automate and pace up onboarding checks. It was an intense weekend, working cross-functionally in a conflict room (just about on Zoom) from 8 am to midnight. Nonetheless, that is considered one of my finest recollections at Brex, exhibiting the true buyer obsession from our management, and the way knowledge scientists can contribute instantly and drive enormous enterprise impression.
IV. Publicity to new instruments
Younger startups are additionally courageous sufficient to attempt a brand new tech stack. Due to this fact, they’re usually the early adopters of recent instruments, whereas bigger corporations may take months (and layers of approval) earlier than even piloting, to not point out the numerous migration prices.
For instance, throughout my time at Brex,
- We began exploring LLM use circumstances in knowledge science two years in the past, and have entry to all main LLM APIs (OpenAI, Claude, and Gemini) internally, open to everybody. Yearly, Brex hosts an inside hackathon and encourages workers to innovate. Two years in the past, I collaborated with engineers to construct an AI-powered buyer suggestions platform to robotically categorize, summarize, and analyze varied unstructured suggestions knowledge. Final 12 months, I constructed a RAG-based chatbot to assist stakeholders retrieve buyer suggestions associated to a particular product function simply. This 12 months, I labored with different knowledge scientists to discover text-to-SQL capabilities with Claude Code and Snowflake CLI. I completely loved these alternatives to use cutting-edge methods to knowledge science workstreams.
- We steadily piloted new knowledge options. For instance, we had been an early buyer of Hex for collaborative and easy-to-share knowledge notebooks. We use Statsig for experimentation and occasion monitoring. We additionally tried varied AI-powered enterprise intelligence software program for higher self-service analytics.
Working at a startup helped me keep knowledgeable of recent applied sciences and undertake them into my day by day workflows. That not solely made the work extra thrilling but in addition saved me aggressive because the trade developed.
On the flip facet, being an early adopter can imply disrupting present workflows and rebuilding infrastructure. It additionally means a much less secure improvement expertise.
Conclusion
So, do you have to be part of a startup or a extra established firm?
Startups supply pace, selection, visibility, and cutting-edge publicity. However in addition they convey chaos, shifting priorities, and the necessity to put on hats chances are you’ll not get pleasure from. If you happen to thrive in ambiguity and wish to speed up your studying curve, a startup could be an unbelievable place to develop as an information scientist.
For me, 5 years at Brex have taught me an unbelievable quantity of information about enterprise and knowledge. I’ll perpetually be pleased about the teachings, the individuals, and the prospect to see what knowledge science can seem like at a fast-growing startup.
