Data Science Jobs in Chicago

Our speciality is matching highly experienced and skilled talent, with world leading organisations who value the hidden insights data scientists can extract from data. Uniquely we understand the bespoke nature of this requirement and specialism, which goes beyond just qualifications in languages and database technologies such as R, Python and MySQL.

View our Data Science Jobs in Chicago here now.

Latest Jobs

Unfortunately we have no live jobs currently in this sector.
Please register for job alerts to be notifed as soon as a new role becomes available.

Harnham blog & news

With over 10 years experience working solely in the Data & Analytics sector our consultants are able to offer detailed insights into the industry.

Visit our Blogs & News portal or check out our recent posts below.

Making It As A Woman In Data Science: An Interview with Ashley Holmes

Meet Ashley Holmes. Senior Data Scientist for a firm working to improve healthcare. Or rather, the healthcare system.                   It’s been an unusual year by all accounts. Most jobs have moved online for the foreseeable future, yet jobless rates climb. Everyone is learning to pivot and accelerating their focus and skillsets. It’s also a time to evaluate where you are in your career and where you want to go. So, from time to time, we find it’s best to hear some stories directly from those in the field.  Ashley's story begins with a desire to become a math teacher which in later years included Computer Science classes. A girl with a talent for math taking computer classes? This is her story: What drew you to Data Science from your original education focus? I’d wanted to be a middle or high school math teacher since I was 12 years old. In college, I discovered part of the math major required students to take one computer science course. I took the computer course my first semester of college, and really liked it. Programming was fun! So, to my Math major, I added a Computer Science minor in which I was the only woman. I recall a course in Operations Research in which we’d used mathematics to answer problems in healthcare by using linear algebra to optimize a design for a staffing schedule. This staffing schedule would used by surgeons for operating rooms. Who knew there was a field where you could solve healthcare problems with math and Data? I didn’t, but now that I knew, I dug in. Enter Binghamton University’s Systems Science and Industrial Engineering Department. Though at the time, Master’s Degrees in Data Science didn’t exist yet. But this program at Binghamton had a concentration for healthcare systems. This concentration had it all – courses for Data Science skills like Statistics, Machine Learning, and Artificial Intelligence.  After some of my own horrifying interactions with the healthcare system in the US, and realizing I could use my skills in Math and Computer Science to improve it, then that’s what I wanted to do.  With a graduate research assistantship from The Watson Institute for Systems Excellence (WISE) at Binghamton University, I found myself in the process engineering department at a large care management organization in New York City. It was there I got some real-world experience using clinical Data collected by the hospital to improve processes and solve problems the company had been facing. I was hooked and so my pivot from Math Teacher to Data Scientist.  It's been 10 years since you started on this path, it seems, what changes have you seen in women in the field and/or STEM focus of young women still in school?  While R and Python are taught a lot more in required courses, there was no such thing as a Data Science Masters Degree when I was in school. Most of the Data Scientist’s I know have Mathematics, Computer Science, or Engineering degrees. Though we did some light coding in my grad school courses, most of my real programming skills have come from my graduate research assistantship and various jobs I’ve had. Talk about on the job training! When it comes to women in the field, that has grown significantly thanks to hackathons, events, and groups tailored to encourage women to enter the field.  What Do You Think Now?  In 2018, I heard about a non-profit hackathon in Boston called TechTogether whose mission was to end the gender gap in technology, which I thought was amazing. I’m also now part of a few professional groups for women in STEM that meetup in person and have conferences (pre-COVID) or at least have Slack channels.  These advances for women in technology have been great, but there is still a lot of work to be done. I actually attended a talk yesterday by Melinda Gates (who was herself a computer science major) about how the pandemic is affecting women and girls, who mentioned that in the late 80’s when she was in school, women made up about 35% of computer science majors, whereas now in 2020 it’s down to 20%.  Wait, it's Declined? Why is it Do You Think? I was curious about this too. So, I did some digging to try and find data on this, and came across this NPR article which suggests that the share of women in computer science started falling at roughly the same moment when personal computers started showing up in US homes in significant numbers. It was at this time, computers in homes were mostly for gaming, and "computers are for boys" became a popular narrative. A 1990 study shows that families became more likely to buy computers for boys than for girls, even when their girls were really interested in computers. As those kids got to college, computer science professors were increasingly men, and increasingly assumed that their students had grown up playing with computers at home. Surprisingly, this extended even to the 2010s, because I only had one female professor in my computer science department; the rest were male. Not that they were bad professors by any means, but it seemed to me even then that it was much more difficult for women to break into the profession and actually succeed. Needless to say, I was shocked (and thrilled!) when I first read the book Hidden Figures, and found out about NASA's women computers who were essential to putting human beings on the moon.  I think more stories like this have come out since I was in school...I also remember hearing that Edie Windsor, who was already a hero of mine for her LGBTQ rights activism, was a technology manager at IBM. As these stories have continued to come out, I think more women have been able to see themselves as able to do these kinds of jobs, and that is part of the reason we are on the rebound. Though 2020 has been an unusual year by all accounts, it is also the beginning of a decade. What do you see for the future of women in data science and what has your experience been? With the prominence of social media now, I think it’s becoming much easier to find women in your field to connect with and ask for advice and support, and I think this is true for both young girls potentially interested in data career paths and professionals already in the industry.   What steps would you recommend to young professionals entering the data professional path or those looking to change careers? Any job or networking trade secrets you wish you'd known before finding your current position?  Being part of a community and making connections with other women in the field has been very helpful both personally and professionally. Join a club: Girls Who CodeGirlstartSociety of Women EngineersCheck out conferences like Grace Hopper and Women Impact Tech. Just knowing that there are women out there with jobs that you’ve never heard of can be really beneficial to believing that you can do it yourself. Look at people with the job titles you’re interested in, and see what they’ve done in the past as far as jobs, education, etc. Network and establish relationships with other women in your field. This is a very valuable tool both for getting a job and for general professional support. Take every opportunity to network that you can; I’ve gotten most of my jobs through networking and knowing people.  As a Senior Data Scientist and a woman what challenges do women still face in the industry and what's something surprising you've encountered that helped you grow either personally or professionally? I think women still face a lot of challenges in the industry. Firstly, there are just so few of us. In most of my jobs (except for my current one), Data Science teams are largely made up of men.  Document your accomplishments throughout your job and bring it with you when it’s time to talk promotions and raises. It is absolutely crucial to be able to speak up for yourself and be your own biggest cheerleader. I used to think that the way to advance through a career was just doing excellent work and waiting for someone to notice you and give you a raise or a promotion. I’ve found that isn’t true at all, and if you aren’t talking about your own accomplishments, who else is going to? In that same vein, finding mentors, coaches, and sponsors is critical. Finding someone who has seen your work and can speak about it and you to other people is incredibly important.  Your Best Advice? My best advice is to apply for the job, even if you don’t think you’re 100% qualified. If you’re looking for a role in Data Science, Harnham may have a job for you. Check out our current opportunities or get in touch with one of our expert consultants to learn more.   For our West Coast Team, contact us at (415) 614 - 4999 or send an email to sanfraninfo@harnham.com.   For our Mid-West and East Coast teams contact us at (212) 796-6070 or send an email to newyorkinfo@harnham.com.  

Why You Should Always Be Learning In Data Science: Tips From Kevin Tran

Last month we sat down with Kevin Tran, a Senior Data Scientist at Stanford University, to chat about Data Science trends, improvements in the industry, and his top tips for success in the market.  As one of LinkedIn’s Top Voices of 2019 within Data & Analytics. his thoughts on the industry regularly garner hundreds of responses, with debates and discussions bubbling up in the comments from colleagues eager to offer their input.  This online reputation has allowed him to make a name for himself, building out his own little corner of the internet with his expertise. But for Tran, it’s never been about popularity. “It’s not about the numbers,” he says without hesitation. “I don’t care about posting things just to see the number of likes go up.” His goal is always connection, to speak with others and learn from them while teaching from his own background. He’s got plenty of stories from his own experiences. For him, sharing is a powerful way to lead others down a path he himself is still discovering.  When asked about the most important lesson he’s learned in the industry, he says it all boils down to staying open to new ideas.  “You have to continue to learn, and you have to learn how to learn. If you stop learning, you’ll become obsolete pretty soon, particularly in Data Science. These technologies are evolving every day. Syntax changes, model frameworks change, and you have to constantly keep yourself updated.”  He believes that one of the best ways to do that is through open discussion. His process is to share in order to help others. When he has a realisation, he wants to set it in front of others to pass along what he’s learned; he wants to see how others react to the same problem, if they agree or see a different angle. It’s vital to consider what you needed to know at that stage. Additionally, this exchange of ideas allows Tran to learn from how others tackle the same problems, as well as get a glimpse into other challenges he may have not yet encountered.  “When I mentor people, I’m still learning, myself,” Tran confesses. “There’s so much out there to learn, you can’t know it all. Data Science is so broad." At the end of the day, it all comes down to helping each other and bringing humanity back to the forefront. In fact, this was his biggest advice for both how to improve the industry and how to succeed in it. It’s a point he comes back to with some regularity in his writing. “It doesn’t matter how smart you are, stay humble and respect everyone,” one post reads. “Everyone can teach you something you don’t know.” Treating people well, understanding their needs, and consciously working to see them as people instead of numbers or titles—this, Tran argues, is how you succeed in the business. To learn and grow, you must work with people, especially people with different skills and mindsets. Navigating your career is not all technical, even in the world of Data. “The thing that cannot be automated is having a heart,” he tells me sagely. Beyond this, Tran stresses the need for a solid foundation. The one thing you can’t afford to do is take shortcuts. You have to learn the practicalities and how to apply them, but to be strong in theory as well.  Understanding what is happening underneath the code will keep you moving forward. He compares knowing the tools to learning math with a calculator. “If you take the calculator away, you still need to be able to do the work. You need the underlying skills too, so that when you’re in a situation without the calculator, you can still provide solutions.” By constantly striving to collaborate and improve, Tran believes the Data industry has the best chance of innovating successfully.  If you’re looking for a new challenge in an innovative and collaborative environment, we may have a role for you. Take a look at our latest opportunities or get in touch with one of our expert consultants to find out more. 

Recently Viewed jobs