By Francie Fink
I recently had the pleasure of speaking with Jordari JD Rene, Assistant Director of Employer Relations and Professional Connections at the University of Illinois Urbana-Champaign. With years of experience working closely with both college students and industry professionals, Jordari is deeply passionate about helping students showcase their talents and connect with meaningful career opportunities. During our conversation, we explored the rapid growth of data science as both an academic discipline and a career path. Jordari shared valuable insights into the skills that employers are seeking in data science students and graduates, and the ways students can position themselves for success in this rapidly expanding field.
Key Takeaways
- LAS in Data Science — 65% of those interested in data science careers stem from the College of Liberal Arts and Sciences in majors like CS + Data Science, CS + Philosophy, Psychology, Sociology, Biology, and more.
- Experience is a Spectrum — Rene believes experience comes in all different varieties. Employers are looking for anyway you put your learning into practice, not just big-name internships or grades.
- Different Data — The market has become more creative, beyond just finance and business, students can explore a variety of paths like sports, surveys and storytelling.
- Communication is Key — According to Rene, you need to know how to interpret and communicate you’re data through visualization and conversation.
- On the Technical Side — Certain tools are must-haves, says Rene. Tableau is your “bread and butter,” SQL is huge for entry-level jobs, and others like Python, HTML, and Java definitely don’t hurt.
What brought you to work here at the University of Illinois, and what got you interested in career development?
I got my start in career development back when I was an undergrad at the University of Central Florida—go Knights, Charge On! I studied Industrial-Organizational Psychology and worked closely with career services as a recruiting assistant and career peer advisor. That experience gave me firsthand insight into the job market, working with employers, and helping students navigate their careers.
After graduating in December 2019, I took a role at a math tutoring company as an assistant director, managing recruitment across multiple centers in Central Florida. But after six months, I realized it wasn’t the right fit. I went back to my mentor, who asked me one question alone: “What’s something you enjoy without even thinking about it?” For me, the answer was clear—college. Not just the academics or involvement, but the whole transition from college to career, or really, college to life.
That realization led me to higher ed. Through my mentor’s network, I learned about the Aspire fellowship at the University of Illinois. I literally traded palm trees for cornfields! I started out working in student affairs, mentoring first-gen students, and then moved into my current role at the Career Center in July 2022. Now, as an assistant director for employer relations, I connect students with companies across industries—tech, consulting, government, nonprofits—you name it. I truly believe all experiences matter, and career paths aren’t always linear. My job is to help students embrace that journey and find the opportunities that fit them best.
Pictured: Jordari interacts with an undergraduate student at a Career Center outreach event.
What majors, minors, and types of students do you work with who are interested in data science careers?
Data science is completely interdisciplinary. We have ten colleges, each with a range of majors that touch on data in different ways. The College of Liberal Arts and Sciences (LAS) is where we see the largest number of students—about 65% of those who visit the Career Center—interested in data-related careers. That’s like CS + Data Science, CS + Philosophy, Psychology, Sociology, Biology, and more.
We also see a strong presence from the iSchool, or the School of Information Sciences, which is fairly new but has quickly become a hub for students pursuing data and information science careers. Our computer science program is highly competitive, so the university created CS + X degrees that blend CS with other disciplines. This can give students more flexibility. But at the end of the day, I always tell students: your major is just one line on your resume—what really matters is your experience. In fact, the National Association of Colleges and Employers (NACE) found that almost a quarter of employers hire any major, and just under a quarter hiring in only majors exclusive to their industry.
Why is data science such an important field today? How would you explain its impact to an incoming freshman or someone who is curious about what data science even is?
I think data science is such a powerful field because it helps display a story. It helps create the back end of solutions, narratives, and decisions that industries, companies, and organizations need. But one thing I’ve noticed with incoming students is that their expectations don’t always match reality. We had about 15,000 freshman students coming in. What I do notice from previous freshmen classes is that the students who are pursuing information sciences, data, analytics… some of them came in with experiences and connections. Many students feel lost if their first opportunity isn’t with a major company. There’s a perception that success in data science is tied to landing a big-name internship right away. But I think students underestimate the power of projects and explorational experiences. The experiences come in all different varieties, including their classes. Some students will tell me “I created this data set for my professor and turned it in–I did well!” That’s it. I say, “Wow, you actually just did a lot more than you think you did. You used R, you turned in a full deliverable.” That’s exactly the kind of applied learning employers value. They’re not just looking for grades; they want to know how you’ve put it into practice.
“Many students feel lost if their first opportunity isn’t with a major company. There’s a perception that success in data science is tied to landing a big-name internship right away. But I think students underestimate the power of projects and explorational experiences.”
What’s a challenge that someone wanting to get into the field of data science should be aware of?
I remember when I was a kid in Florida, someone came into our classroom one day yelling, “Data is the future! You have to love it!” And they weren’t wrong—data has become central to everything. But now, because of that early push and the rise of AI, the field has become saturated. Students today face challenges not just from extreme talent and competition at a younger age, but also from companies outsourcing work. That reality can be discouraging, and I want to acknowledge that when a student comes to me. But there’s still a strong demand for students who are ready to put their skills into practice. Companies are looking for data science graduates immediately, even at the bachelor’s level.
There’s a scarcity thing going on where to a lot of people, a bachelor’s degree is like the equivalent of a high school degree. I want students to approach the field with an open mind. It’s not as simple as getting a degree and automatically becoming a data analyst—it’s not that linear. The market is a little more creative now, students can get a little creative, beyond just finance and business. Maybe they’re analyzing sports performance data, working with school outcome surveys, or even helping other organizations tell stories through qualitative and quantitative research.
Pictured: Jordari works with students on mock internship interviews.
Are there specific skills or extracurricular activities that can help young data scientists stand out in the job market?
I think there are definitely some tricks when it comes to standing out in data science. First, you have to enhance your skills, but not just in technical ways—you also need to know how to interpret and communicate what you’re doing. If you can’t explain your data findings to a five-year-old, a ten-year-old, or even a recruiter with zero technical background, you’re going to struggle when working with stakeholders. Visualization is huge. You should be able to take complex data and translate it into something digestible. And beyond that, students should be open-minded about applying their skills in different ways. I’m not saying every data science student needs to be an entrepreneur, but there are so many opportunities to gain experience. Help your student club analyze event attendance data, assist a local business with an assessment, or support a campus researcher working with survey data. Those real-world applications of your skills matter just as much as what you learn in class.
On the technical side, certain tools are must-haves. Tableau—your bread and butter. Learn it, love it. Then there’s SQL, which is huge for entry-level jobs. Python, HTML, and Java? They don’t hurt either, and practicing on sites like LeetCode is a great way to sharpen those skills. But at the end of the day, practice makes progression—not perfection, but progression. The more you apply your skills outside of the classroom, the stronger you’ll be. You taking on a summer project, consulting for a club, or assisting with research–that’s applying those skills.
“At the end of the day, practice makes progression—not perfection, but progression. The more you apply your skills outside of the classroom, the stronger you’ll be. You taking on a summer project, consulting for a club, or assisting with research—that’s applying those skills.”
What are a couple of first steps for students that want to see what’s out there (in terms of companies, positions) and get their name out there?
That’s a really good question. I think it’s important that maybe the first thing that a student should understand is first, their professional core values. I define a core value to my students as any professional or personal attributes, internal or extrinsic motivations. You have millions of these values. Not just salary or work-life balance—I’m talking about deeper values.
I think as a student, as anyone stepping in to this process, they should start thinking about what they envision. Take that time, take a breather, close their eyes and ask themselves, what does that look like for them? Do you thrive in teams or prefer working independently? Do you want leadership opportunities, or do you need a workplace with clear growth paths? What kind of impact do you want to make? These values will evolve over time, because you start learning throughout those areas. But I think it’s a good first step.
The second step is knowing what you’re good at and what you’re not. If you’re considering an analyst role, break down the typical tasks involved and highlight areas where you feel strong and areas where you need improvement. Then, you can hyper-focus on some of those pieces, improving throughout their four year, five year, six year tenure at a university.
Third, look to your community. Your peers—classmates, friends, even group project partners—are your first network. Ask them questions: What are you interested in? Have you done any internships? What skills do you think are most important in our field? It can be simple, like getting to know a friend. From there, you can start understanding what some of the common traits are for lines of work you’re interested in. Your peers connect you to professors. Professors connect you to their industry contacts. Industry contacts lead to recruiters.
Those are my big three on knowing your values.
While we’re on networking, do you have any other pointers?
Networking is fuel. I believe that networking is not just having connections to open doors for you. A network is an entire community of like-minded individuals ready to make the same impact as you or support the impact you’re going to have. It’s not just hitting your LinkedIn and sending cold openings to employers.
And following up is the most important piece. I’m so big on elevator pitches, because I feel like there is nuance to the way we are always taught to use them. I always emphasize that an elevator pitch is not just a one-way speech—it’s a two-way conversation. A lot of us were taught to rattle off our name, major, and career interests in under a minute, then stop talking and hope the other person is impressed. But that’s not how real connections are built. A strong elevator pitch isn’t just about introducing yourself, it’s about starting a dialogue. So when you deliver your pitch, don’t just end with an awkward silence. Say something like, “I really enjoyed talking with you. Could we connect again in a couple of weeks?” or “I’d love to hear more about your experiences—when would be a good time to follow up?” When you have questions, they don’t feel like it’s scripted.
Pictured: Jordari and colleagues at a networking event.
What are the most important soft or transferrable skills that folks in data science should have?
Yeah, so as you brought up, being able to interpret data is huge. But beyond that, when you talk to employers, they’re looking for certain core values and soft skills that really stand out. I mentioned NACE, which we follow. NACE has these amazing eight core competencies. One of those things is being able to be adaptable to new technology. Employers are in a race to stay ahead, right? They’re competing with each other to bring in the latest tech and get movement in those areas. So they want to see that you’re not just learning what’s out there now, but that you’re willing to continue learning in the field.
Another big piece I like to bring out to individuals is relationship building. And being able to connect with individuals. There are consulting roles that are all relationship building. From there, your background and expertise help. And I think another piece that people sometimes overlook is accountability—just being able to acknowledge when something didn’t go right. It’s part of the research method. You don’t need to be 100% correct on a consistent basis, but you need to be able to step up and take that sense of accountability for what you need to change, help interpret, or understand better.
Are there any resources you’d like to point students toward?
- O-Net Online has an industry and career profiler for any position you would like to pursue.
- The NACE Job Outlook Report 2025 is a great resource for those who’d like to better understand what employers themselves are thinking.
- Dice Tech Job and Internships is a great starting point for folks looking for an internship or job in a data science or tech field.
- The What I Can Do with My Major tool is an amazing research tool to understand strategies you can do with your data-based major. This is free for anyone, but you must access via our website.
Learn More and Get Involved
Contact Jordari if you’d like to learn more or walk through resources, or schedule an appointment here.
Contact the Office of Data Science Research if you’re aware of other people or resources we might profile here. ODSR is a campuswide convening organization that facilitates collaborations, resource sharing, and public engagement focused on data science research activities at the University of Illinois.