Joy James is a Utiva alumna and a master's student at the University of Bradford, United Kingdom. She previously worked as a Data Scientist at the prestigious Sterling Bank in Nigeria. In this interview, she shares the challenges she faced while trying to break into the data science profession and how she successfully overcame them.

How did you become a data scientist?

The first time I learnt of data science as a career was at a tech conference organised by Zenith bank in 2019. Data science as a career seemed very attractive to me, so I decided to explore it. After the conference, I started looking for how to learn data science. A friend recommended Utiva, so I enrolled in the program. Joining the Utiva training is one of the best decisions I ever made: it gave me clarity, connections, a community and the headstart I needed to start my data career.

I got a job with Sterling Bank as a graduate trainee shortly after the data training at Utiva. While at training school, I noticed the Bank had a data office where data scientists, data analysts and data engineers worked. I wanted to be posted to the data office after training school, but instead, I got posted to one of the branches. I contacted HR and told them about my skills and how valuable I would be to the data office. 

That was a tough period in my career, and I believe every learner goes through that phase. You start off really excited about this new skill you are learning; you tell your family and friends; you post all your projects on LinkedIn, but after a while, you notice that you’ve been putting in a lot of effort, but the job is not forthcoming. It starts to feel like your hard work is not yielding results. I found myself saying things like "where is the job?" "These tech people lied to me". Everything started getting to me, and my zeal waned. 

Working at the branch left me with little time to learn, but I was determined to not give up. I signed up for DataCamp and used it to hone my data skills with every spare time I got, e.g; during breaks, while commuting, or after a meal. I also decided to look for people to teach. I wasn’t an expert, but I knew my "little" knowledge would be valuable to someone who knew nothing about data. Fortunately, Utiva was looking for volunteer cohort captains, so I jumped on it.  

One day, someone from the data office reached out to schedule an impromptu interview with the data office. Looking back, I'm grateful I didn't let the lack of motivation get the best of me, but I continued honing my data skills. I was confident and prepared to answer the interview questions because I had not stopped learning. After the interview, I didn’t hear from HR for a while. I started to consider switching to another career. It was hard, and frankly, I was tired, but I kept hope alive. In May 2021, I was finally asked to resume at the data office. This was after working with the Bank for over a year.
 

That’s an interesting story. Why did you decide teaching would be a good outlet for you?

When I was in University, I used to host tutorials in class. While teaching people, I realised that the more I explained a concept to someone else, the more I learnt about it. So, I knew teaching was a strength that helped me solidify my knowledge. I also felt it would be a great way to sell myself during interviews and establish my authority as a data professional. People believe gaining experience is limited to getting a 9 to 5, but that’s not true. You can gain valuable experience from working on personal projects, volunteering, teaching others, etc. The opportunities are endless; do not limit your experience to a job,

You mentioned earlier that you wanted to pivot into an "easier" career since data jobs did not seem forthcoming. Tell us more about that. 

So, I was actively applying for jobs, but my efforts seemed futile. I was also unhappy at my company because I was posted to the branch. Looking back, I am thankful I worked in the branch because the experience sharpened my domain knowledge. When I moved to the data office, people frequently asked me questions because I had an in-depth understanding of operations and I could notice patterns or detect when data was “off”. I would not have easily known these things without working at the branch. 

My point is, whatever knowledge you have is very relevant to your data career. Don't disregard your non-data experience; they will come in handy. 

What was it like when you moved to the data office? 

Moving to the data office was a big deal for me, but it was the start of a new journey. My teammates were way more experienced than I was and it affected me. I battled with impostor syndrome; I felt dumb and unqualified to be there. I had to constantly remind myself that these people started earlier than I did, and my focus should be on learning and contributing to the team.

To back up my words, I picked up the “dirty” work that no one did: creating presentations/ proposals, conducting research, and looking for issues with the data. I frequently asked questions like: why is a code written a certain way? How is a task contributing to the bigger picture? I also went out of my way to ask for work, and gradually I strengthened my skills. 

Were you improving your knowledge only on the job? What are other ways people can hone their data skills?

I have been consistently taking online courses since 2020. For instance, if I see a query I do not understand at work, I quickly google it and look for a short course or tutorial to understand the concept of the query. After doing this, I look for a project I can apply my knowledge.

Secondly, I didn’t stop teaching other people, instead, I got better at it. Whenever a student got stuck on a personal project, I would help them excitedly.  

Thirdly, I always work on projects. Honestly, I only take online courses because I need specific knowledge for a project. I always encourage people to take this route; start a project, and if you get stuck, take a course to get the solution. Don’t just take a course without applying the knowledge to a project. 

Lastly, look for mentors. A mentor doesn't have to be the head of data or a senior data scientist. It can be someone with a skill you want to learn or someone a level higher than you. Whatever the case, always ensure you have people you can run to for help.

How do you get projects to work on? 

There isn't an ultimate guide to getting projects, but I'll share a tip that worked for me. I search for projects I am interested in on places like GitHub, Kaggle or even in-house projects done by other members on my team and try to replicate their projects. I don't replicate the project word for word, but I use it as a source of inspiration. I review the code and look for ways to rewrite it for readability and efficiency.

Any tips for someone learning data science?

Everybody's journey is unique and filled with rough patches. Learn to embrace the highs and lows. Also, learn to be patient with yourself. If things are not going how you would like, don't give up; keep at it. 

Your first job might not be what you want, but ensure you're maximising available opportunities. Also, be an active member of a community; emphasis on being active. 

These tips will help you.