The 79th episode of Datacast is my conversation with Jessica Cherny — who currently runs the data analytics team at Ironclad, a series-D digital contracting startup.
Our wide-ranging conversation touches on her upbringing in a triplet, her education in data science at UC Berkeley, her current journey building out data analytics and cultivating a data-centered culture at Ironclad, leveling up as a data analyst, building community and mentorship with Data Angels, and much more.
Please enjoy my conversation with Jessica!
Listen to the show on (1) Spotify, (2) Apple Podcasts, (3) Google Podcasts, (4) TuneIn, (5) RadioPublic, and (6) Stitcher.
Key Takeaways
Here are the highlights from my conversation with Jessica:
On Her Upbringing
A big way to narrow down my upbringing is hustle and grind. My parents immigrated to the US at a very young age, the same age I am in their early 20s. They had to start all over as immigrants. Even though they were highly educated folks in Russia, they couldn’t learn English as fast as they could have and had to subsequently start from the bottom in America — working as pizza delivery workers/sandwich makers and cramping in an apartment with my mom’s parents and grandparent to make the American dream happen.
From those experiences, my parents have always passed on to my sisters and me the value of education. They saw education ultimately as the golden gate to let you assess the ranks of society. We always felt how important it was to study hard, make education a big priority, and rise up those ranks. My dad told us how he had studied on the floors of Barnes and Noble and read the newest/latest programming books or how he snuck into SF State’s programming classes. My mom also took some extension courses to grind and study. Growing up, we felt like we had that kind of pressure coming from them, but also from ourselves to make our parents’ immigration worth it in some way.
A part of growing up in a triplet in that realm is that you always have somebody to compare yourself to. If I’m doing better at a subject or if my other two sisters are doing better in a certain subject/sport/activity, we are always asking ourselves: “Why can’t we be as good as the other ones?” We always have this friendly level of competition and try to level up to each other. I think that has made us strive to be better, work harder, and constantly up-level ourselves.
My sisters and I are very close. We finish each other’s sentences. We know the best and worst parts of each other. We majored in similar fields, shared classes, and roomed in college together. We share similar ambitions and all work in tech. We all are interested in data in some way, but we all interact with data differently in our day-to-day jobs.
On Studying Data Science at UC Berkeley
When I came to Berkeley, I was a computer science major. Given that my father had a background in software engineering and we grew up in Silicon Valley, there was a big push for women to go into computer science or some technical fields. I tried out computer science, software engineering, and mobile development in my first year but didn’t love it. Coming back in my second year, I took a new class called “Introduction to Data Science,” in which we programmed in Python, learned statistics, and did some data projects related to economics. So data science felt like an encapsulation of all my interests. That’s why I kept pushing for that direction and took a bunch of coursework in statistics, economics, and programming.
I loved the data ethics class, which was a requirement to graduate. It’s so important for any technical majors to take an ethics course in order to contextualize how the work you’ll be doing might impact people. You definitely want to be careful with data privacy — what data can we glean from user behavior and how not negatively impact them. Data privacy has been studied for decades. It just comes in different forms with different generations and technologies.
Data science has become one of the top three most popular majors at Berkeley. People who aren’t so jazzed about computer science or economics flock to combine both patterns and interests into data science. The major allows you to combine data science plus an interdisciplinary field (like Russian literature, sociology, or mechanical engineering), making it very flexible.
On Internship Lessons
Try a bunch of different roles: I wasn’t unsure if I wanted to be a mobile developer, a consultant, or a data analyst. So I did all three of them. If the internship felt like a lot of labor work and every day felt long and not passionate, then I couldn’t see myself doing that. I felt the most passionate in the data analytics internship.
Make expectations of what success looks like very clear with your manager: At the onset of your internship, you want to benchmark what success looks like and what deliverables are expected of you at the end of the internship. You want to ensure of hitting them and going beyond.
Form a close bond if you can with your manager: You’ll never know if that person will be a sponsor, a coach, or a referrer for your future internships, return offer, or another full-time job. Hopefully, they have a big network and a big say in your success. If you get on their good side, that’d be valuable.
On Joining Ironclad
The big organizations have more capacity to onboard people very early in their careers, whereas startups just throw you into the deep end with a huge learning curve. The latter excites me. Initially, I wasn’t looking for a startup and was scared of all the horror stories about the early days of startups (sleeping at desks, working crazy hours, zero work-life balance, etc.). I always thought I wasn’t qualified enough to do this right out of college. I felt like I had to earn my stripes before contributing anything to a startup because startups are notoriously less hand-holding. In contrast, big companies provide much more mentorship, onboarding, or training.
However, this was a unique opportunity at Ironclad that my mentor Amit Kumar brought me. Ironclad was a hot young Series A startup when I first talked to them. They offered to create the very first data analyst role just for me. And that was so exciting that I couldn’t say no to it. No matter how the startup performs, even if it fails, that’s still such a big growth opportunity. I’m young enough when it’s okay to take that kind of risk. After interviewing with the folks there, I thought that they were humble, smart, welcoming, and had a good head on their shoulders. So I decided to jump right into the pool!
On Digital Contracting
Digital contracting means that the entire contract lifecycle lives in a browser-based system, a product, or an application. The contract lifecycle includes contract creation, contract editing, contract approving, email communication between different parties, signing, archiving, and storing the completed contract into a repository. Ironclad does all of this in one system.
Before digital contracting, there was a disparate universe. Contract lives in one system, but you communicate over email and archive things in different storage systems. Nothing was unifying digital contracting. Ironclad is the first flexible platform to unify everything (the negotiations, the operational insights, the editing experience, the contract creation, etc.) for the in-house legal team. The platform is meant to be used by people within successful tech companies to execute contracts quickly and get the most people to be onboarded as efficiently as possible.
Given my role in analytics, I talk to customers specifically about their data reporting needs. Legal is often seen as a cost center, but it is at the center of the business because companies need legal for everything (hiring, marketing, sales, patent IP, etc.). Ironclad is currently working on a really good solution to help in-house legal teams report their efficiency metrics to executives at their respective companies. Ironclad helps these teams illustrate efficiency and load increasing to show their value.
On Bringing Analytics To Ironclad
It was nerve-wracking being the first analyst at the company. I started out as a data analyst embedded on one of the product teams, and we had three at the time. I did some analysis for the workflow designer team. That quickly grew into more product analytics for the other two product teams — repository and editor. Then that kind of grew into doing more analytics for both product and customer success functions. Eventually, I became Jessica, the data person for all sorts of ad-hoc analysis.
Not that there was no one doing data elsewhere. Before that, Ironclad had data-adjacent people like sales and marketing operations analysts. But there was nothing to unify them. Having a data analyst sitting purely in that role was new, so I sit mostly between the product and customer success realms.
To get buy-in, I found the low-hanging fruits to work on. Early on, everyone was data-hungry. Ironclad, at that point, had been using data heavily. The project management team grew quickly and tried to understand usage adoption patterns. As their data requests grew, there was a natural cross-pollination and socialization of my work across customer success. Customer success needed a quick solution to get data on customers efficiently. As our customer lists increased, we couldn’t scale the customer success organization enough. We needed a data tool like a dashboard to allow customer success managers to get their metrics quickly. I created a dashboard that serves all of their needs to get a detailed view of customers’ product usage. That dashboard blew up and was a big catalyst for having organizational buy-in for analytics.
On Leveling Up Her Career
I don’t have a direct data analytics manager. My managers have been the VP of Product and Engineering, so I don’t have a true idea of what great data analytics should be expected from somebody way more senior than I am. I have an idea of what good deliverables look like in general from my bosses, but it’s challenging to define my own specific data analytics goals. Self-motivation is always kind of a balancing act.
Reaching out and expanding my network has been a big way to temper a big learning curve. I decided to reach out to some senior data folks on LinkedIn and asked what kinds of analysis they worked on early in their careers. It’s helpful to get that outside perspective and combine my current managers’ perspectives with leadership and stakeholder management.
On Cultivating A Data-Driven Culture
Building a data MVP: This is similar to what good product managers do. You have to develop the first iteration of your data deliverable quickly enough while paying attention to the usefulness of that deliverable to your stakeholder. If somebody comes up to you with a problem, have a quick way to drop down the requirements and create the very first iteration (it can be something simple like writing SQL code to make a table appear and ask for the schema correctness). Having the check-ins and brainstorm sessions is really good before doing advanced analysis.
Socializing and iterating the MVP with data-interested cross-functional teammates: When you meet with a cross-functional group, they will probably have data questions and insights that will cause you to think in a new way that you haven’t thought of before and, therefore, enhance your analysis. It will also help you build credibility with your coworkers because when you socialize your work in front of a bigger crowd, they’ll know you as the data person (which I found in my experience).
Scaling analytics to innovate faster: Once you’ve developed different features to be added to your analysis, you want to either automate them or make an advanced analysis (like using data science modeling instead of basic data analytics). Once you have iterated through the data multiple times in your MVP and gathered inspiration from cross-functional stakeholders, you are ready to productionize the data science and analytics you have worked on.
I think what has helped me is that I am a kind person, and I also have an opinion. When I first onboarded the company, I was still in the phase when it was scary to go into meetings and talk. Over time, I developed the confidence to have a seat at the table and be dependable when somebody discusses potential analyses that they want to explore. Having good write-ups and socializing the analytics in a Slack channel within a quick turnaround time builds a good culture. It’s important to prove that you are approachable. To drive a data-driven culture, you want people asking for data, not being scared of it.
On Building the Data Angels Community
From a young age, women are discouraged from science and math. We are socialized to see those subjects as male-dominated, and it becomes a self-fulfilling prophecy of just more men in the field by default. I think women are scared to break into the boy clubs sometimes. For me, I’m a very headstrong person. I am proud of my work ethic and want to show the boys that I can be a good data scientist or data analyst with hard work.
Cultivating that confidence in a safe space is hugely important. Seeing other women doing that and setting good examples is why Data Angels is needed. It’s a safe space for women in data to learn from each other in a chill Slack community. We have fun sharing our experiences, resources, and opportunities, such as job postings and Ask Me Anything panels. I think it’s just nice to have this kind of community where you can see others like you and have a genuine bond with people.
People who are genuinely interested in the community will naturally bubble up to the top by posting actively and staying engaged. One great example of this happened when I posted an intern position at Ironclad for the summer and received a response from somebody within a couple of minutes. She went through our whole hiring ground, and we hired her amongst many other interns who also went through the hiring process. So it showed how genuinely interested she was, and I’m so glad she came into the Data Angels community. That’s what the Angels are about — connecting people in the data community to opportunities and other like-minded data folks.
On Data and Fashion
I love conceiving makeup and fashion in my personal life. While growing up, I was obsessed with (and still am) fashion and the beauty industry. I wanted to be a fashion magazine editor like Anne Hathaway in “The Devil Wears Prada.” But then my immigrant parents were like: “No sweetie, you gotta be a doctor or a lawyer or an engineer (typical immigrant things).” But that passion still stays, and I’m glad that I found data analytics as a career path since I can apply data to virtually anything.
I love the idea of applying data science to makeup and potentially getting an application like figuring out my correct foundation shade match on my skin or trying on makeup virtually. I also browse through many different retailers’ websites and would love a really good recommendation engine that points me to the clothes I want to buy (the same way that Netflix and YouTube recommend me content). Besides Stitch Fix, there’s another app called The Yes that is limited to more designer retailers (not so much fast fashion and ready-to-wear outfits). I think it’s possible with data for such an engine.
Timestamps
(01:55) Jessica shared the formative experiences of her upbringing — being born in a triplet with two other sisters and growing up in an immigrant family from Russia.
(05:45) Jessica shared her experience being part of UC Berkeley’s first cohort of Data Science majors.
(09:56) Jessica talked about her campus involvements with student-run organizations such as the Mobile Developers of Berkeley and the Data Science Society at Berkeley.
(13:02) Jessica walked through her participation in initiatives like researching the CITRIS and the Banatao Institute, sitting on the leadership board of the TAMID Group, and being an Accel Scholar.
(15:31) Jessica shared valuable lessons from her summer internships.
(19:08) Jessica discussed her decision to join Ironclad, a Series D digital contracting startup building software to take legal teams to the next level.
(22:39) Jessica provided a brief explanation of digital contracting for the uninitiated.
(24:59) Jessica talked about challenges that in-house legal teams typically face and how Ironclad helps address them.
(27:04) Jessica gave a tour of Ironclad’s Contract Lifecycle Management software offerings.
(30:00) Jessica walked through her journey of building the analytics function from scratch and providing data insights to inform business decisions cross-functionally.
(33:40) Jessica shared tidbits about her time management and goal-setting systems.
(34:45) Jessica walked through the end-to-end data analysis process for Ironclad’s first legal analytics benchmark report analyzing economic trends caused by COVID-19.
(38:38) Jessica discussed the learning curves as she took on bigger analytical responsibilities at Ironclad.
(43:05) Jessica unpacked her 3-level framework for building a data analytics culture from the ground up.
(48:07) Jessica shared concrete advice on positively influencing a company’s culture to be data-driven.
(50:27) Jessica unfolded the drive behind creating the Data Angels Community, a Slack group connecting women interested in data to resources for support, education, and opportunities.
(52:25) Jessica revealed her community playbook to engage the members of Data Angels.
(57:01) Jessica shared a bit of her guilty pleasure in using data for beauty and fashion.
(01:00:44) Closing segment.
Jessica’s Contact Info
Mentioned Content
Resources
“How to use contract data during COVID-19” (Ironclad Report)
“Building data analytics culture from the ground up” (Women In Product 2020 Talk)
“Building a data-centered culture at Ironclad” (Ironclad Article)
People
Emily Robinson and Jacqueline Nolis (Co-Authors and Co-Hosts of “How To Build A Career in Data Science” the book and the podcast)
Cassie Kozyrkov (Chief Decision Scientist at Google)
Shreya Shankar (Ph.D. Student at UC Berkeley and Entrepreneur-In-Residence at Amplify Partners) (Check out my interview with Shreya as well!)
Book
“Everybody Lies: Big Data, New Data, and What The Internet Can Tell Us About Who We Really Are” (by Seth Stephens-Davidowitz)
Notes
My conversation with Jessica was recorded back in May 2021. Jessica is now a Senior Data Analyst and Ironclad's Data Analytics team has grown to 4 so she is no longer a 1-woman show! Also, the Data Angels Slack community has over 500 members in it now!
About the show
Datacast features long-form, in-depth conversations with practitioners and researchers in the data community to walk through their professional journeys and unpack the lessons learned along the way. I invite guests coming from a wide range of career paths — from scientists and analysts to founders and investors — to analyze the case for using data in the real world and extract their mental models (“the WHY and the HOW”) behind their pursuits. Hopefully, these conversations can serve as valuable tools for early-stage data professionals as they navigate their own careers in the exciting data universe.
Datacast is produced and edited by James Le. Get in touch with feedback or guest suggestions by emailing khanhle.1013@gmail.com.
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