My backstory: I did an MBA in data analytics and then a PhD in organizational psychology. I tried to find a role in academics but found it was not for me. I pivoted into data analytics and data science when I realized the work I enjoyed most revolved around organizing and drawing insight out of complex data. Also, I just enjoy data storytelling.
What I do now: I work as a computational social scientist for Interos,ai., a unicorn company that does data science on supply chain resilience. Long story short, I work on a team of data scientists, machine learning engineers, and developers. Together, we build products that facilitate deep, facile knowledge about how to make supply chains more resilient. I also do teaching and community building work for Correlation One’s Data Science for All, a fellowship program designed to skill up Black, Latin, LGBTQ+, and other underrepresented people for careers in data science roles.
What am I currently working on: Honestly, I am working hard to build up my own skill set right now! I have a non-traditional background in data science, so I am trying to learn more about development practices and continuing to boost my Python skills via a training program from Coursera. I’m also enrolled in an investing course by Arlan Hamilton so that I can better understand the venture capital space. To me, continuing to broaden and deepen one’s skill set throughout a career is important.
One part of my journey was figuring out where I wanted to go. I pivoted to data analytics and data science in late 2018 because I saw that field as a place where I could excel and where there were plentiful jobs and opportunities. I had done a lot of scripting and working with coding languages in my previous role [R, Stata, SAS, and SQL]. I also love probability and statistics and trying to find patterns in data. So all of those pieces felt a lot more like data science than did a role like a developer or software engineer.
Skill development was challenging. I spent a lot of time looking for roles and opportunities to work on projects that would help me build my skills. Another thing that was key for me was taking time (at least a couple of hours) every day to do something to push the needle forward and move me one step closer. For me, I needed not only to be working on a full-time role but also devoting time on nights and weekends to skill development.
Getting started in my career was a slow go, and I had to do quite a bit of work going through staffing agencies. I was eventually able to get a start both with an insurance company and also through a mutual friend who hired me to do some freelance work.
The biggest skills I think about in my current role are:
Technical skills – these are skills I have where I can show someone I have done them and know how to use them. These can include general skills (e.g., writing code, statistical analysis, EDA) or tool-specific skills (e.g. the requests package in Python, Tableau, Google Cloud Suite).
Learning skills – the tech space changes so fast. Success hinges not only on knowing enough about current tools to be effective, but knowing how to quickly get up to date on a new technology or knowledge base is essential.
People skills – there’s a consistent misunderstanding about tech roles and people skills. People who know how to effectively communicate are so critical. Knowing how to manage professional relationships with teammates is critical, because they will help you when you need it. Knowing how to have a frank, effective relationship with your team lead is also critical. And of course, being able to communicate with stakeholders is also key. There’s a lot of ways to develop these skills, but one of the things I did to develop these skills was to work on small, low-stakes projects with other people trying to learn.
Oh do I have some resources to share! Three excellent starting points.
If you are new to thinking about computer science and coding, this book will help you get a sense of how to think in a programming language. https://bit.ly/thinkpythonsnek
One of the most important things you can do for yourself as a data scientist is learn how to communicate data visually. There are books on the topic, but https://www.chartr.co/ remains one of my top picks for an easy, daily dose of material that gets my brain in gear to think about how I would visualize complex data.
Freecodecamp.org is maybe the most overpowered, underestimated training program on the internet today. They updated their 2021 data science roadmap here. Further, they have six certifications here related to tech roles: https://bit.ly/freecodecampcert. All of them are excellent and great to get a person started down this track.
In my last role, one big challenge I faced was actually my own beliefs about myself. I am an overachiever and a people pleaser. I worked in a role where the expectations were demanding and there was often far more work than one person could get done. I struggled to hold it together because I had internalized that I was not good enough to get the work done that was being asked.
Instead, I needed to learn to advocate for myself. I needed to be able to tell my team lead when too much was too much. To overcome this situation, I took advantage of the company’s employee assistant program as well as my career mentors to get some coaching about how to have a conversation with myself and with my team lead around setting and enforcing more reasonable expectations for my workload.
When I was younger, I misunderstood the domain of computer science in general and specifically data science. I wish I had known how much joy I now find in it. I am not sure that there was anyone in my life who could have told me that, but I do wish I had been more open to learning about computer science earlier in my life.
I would love it if people followed me on Twitter! I try to post regular content about data science and free resources to help people get a leg up in their data science careers.
Made with ❤️ by Veni Kunche.