Top 5 Questions to Ask Yourself as Your Reflect

Photo from Marc-Olivier Jodoin on Unsplash

If 2020 taught me nothing else, it taught me when to take a step back and reflect on what was happening in my career. At the start of the year, I opened a OneNote and took notes about every project, significant decisions, and outcomes. I especially did this when I would be the lead data scientist and developer on the project. This notebook allowed me to have targeted reflection sessions every month to understand myself and my position better. I could also bring these notes into 1:1’s with my manager to discuss my accomplishments better. During these reflection sessions, I asked myself several questions. Here are my top five questions to ask as you are looking back on your achievements:

1. What did you enjoy?
2. What did you learn?
3. What could have gone better?
4. What feedback did I receive?
5. What are your opportunities for growth?

What Did You Enjoy?

As I take my notes and reflect on the work I have done, I like to ask myself: What did I enjoy the most about that work? I have found this to be a helpful question to ask as it has given me more insight into what areas of my job I enjoy doing and what I do not. Utilizing this information, I can then speak with my boss about getting working on projects that align better with my career goals.

When I started the position I have now, I was unsure where I would end up, but I did know I enjoyed working in data science and collaborating with other individuals. As I began taking notes on my work and reflecting on the projects I did, those conversations with my boss helped shape the role I am in now. I expressed to my boss that I enjoyed working in a leadership role and would be interested in taking on more work in that space. Highlighting that, I was able to move into a team lead role a few months after joining.

As I have continued in this position, I continue to discuss what goes well, what doesn’t, and where my interests are so, he can help guide my career in the correct direction.

What Did You Learn?

When I am looking at a large project I have completed, I want to note down what I learned from this experience. No matter how small, it is essential to highlight any takeaways from the work. Since data science also incorporates many soft skills, I often find myself noting down improvements in my communication with colleagues or learning about a new aspect of the business. Even if the accomplishment may seem trivial to you now, you will have an exhaustive list to use for your year review.

One of the first big projects I lead had me interacting with my team, an international team, and then the contractors we had hired to do the work. The project’s original goal was to lead the contractors in work and provide assistance where needed. Unfortunately, they didn’t get access to our workspace until the last week of work on what was supposed to be a two-month project. As I assessed the situation, I learned several valuable lessons that I took away from this project. I learned how to:

  • Develop a plan and be able to reevaluate and replan when things go astray,
  • Distribute work across different teams depending on who was capable of each part,
  • Develop a CI/CD pipeline leveraging compute clusters that would run large workloads in under 3 hours,
  • And how to make sure the significant milestones were hit by the end of a project.

This project wasn’t perfect, and the outcome could have been better if we had gotten our contractors access earlier. The lessons I took away from that work helped me understand how to be a better team lead for my group and collaborate with different individuals to accomplish a common goal.

No matter how big or how small, there are always lessons to learn from your projects. Note down those lessons for later reference. Even the projects that do not go as planned can teach you so much. The projects that have the most significant challenges to overcome are the ones that will set you apart in how you went about tackling the situation. These lessons will help as you go to explain your growth as a data scientist, developer, and leader.

What Could Have Gone Better?

Now that I have examined lessons learned, it is a great time to look at what could have gone better. Recognizing where you made mistakes or didn’t perform well can show you the right areas for improvement later on. Before I talk with anyone else, I first reflect on the work myself.

Thinking back to that project I explained earlier, the first thing that could have gone better was ensuring we could get quick access to the contractors’ workspace. Due to this issue, other people needed to be pulled off their work, and projects were delayed to reprioritize the work that was supposed to be done by contractors. Unfortunately, though, access issues to our environment were not something I could control.

One thing I could control was how they implemented the work. The contractors wrote a good portion of the work was written in pure SQL, but the team mainly works in Python. This design decision was based on the fact the contractors worked mostly in SQL. What could have gone better? I could have expressed this fact to the contractors and asked the code to be translated to Python, but at the time, it did not occur to me.

Taking note of these types of situations allows you to reevaluate how you approached the problem and where you could have changed your approach.

What Feedback did I Receive?

Another way to determine areas for improvement is by asking. I often ask my close colleagues, my boss, or my mentors who had assisted on a project for their feedback about specific aspects of the work. I do this because they may have seen different things that I thought went okay, but they could have been done differently from their perspective.

Another good reason to ask for feedback is to learn more. Often, the feedback I received is not about my leadership decisions but based on my code. This feedback allows me to think about my design decisions, understand what areas could improve, and learn about different techniques used to get the same result.

Getting feedback can be a great learning experience, especially in data science. Discussing your colleagues’ techniques and getting their input can give you new ideas for how to continue your analysis or adapt it. But remember, feedback does not need to be taken as 100% fact. Sometimes the feedback is constructive and useful. Other times, the feedback is just a suggestion. Understand the difference between what feedback must be implemented and what feedback falls in that suggestion category. The more feedback you receive, the easier it is to tell the difference.

For instance, when my team does code reviews, we leave comments in the pull request. The comments state what needs to get changed for the pull request to be approved. If a comment is a suggestion that does not need to be implemented, but you may want to consider it, we state that.

“This is a suggestion…”
“You can consider…”
“Just a thought, but, have you tried…?”

Where Are Your Opportunities for Growth?

Now that I have reflected on my work, I know:

  • What I enjoyed,
  • What I learned,
  • What could have gone better,
  • And, what feedback I received.

Taking all this information into account, the final step is to understand my areas for growth. As I mentioned earlier, taking notes on your accomplishments can help you in your career. Now you have a good starting point for what is next. From this exhaustive list of notes, you can determine if there is a set of skills you would like to improve on, the best way to improve those skills, and how you can utilize those skills moving forward.

Continuing with that same project example from earlier, I noted down valuable leadership skills that I wanted to improve on. Through that work, I realized the importance of collaborating effectively with different teams. The skills worked well in that project, and I saw how they could apply to the broader team.

We had a data engineering team that we knew of but did not interact closely with at the time. Every time we had data problems, we either made a workaround or waited for a solution to appear after someone heard of it through the grapevine. Instead, we needed strong collaboration between the teams to solve our data quality problems effectively.

Instead of ignoring the problem, I took the skills I started to learn in that project and applied them. By year’s end, I had gotten the two teams working closely together and teaming up to solve issues. This was a significant growth area that I may have missed if I had not evaluated my previous project and continued to improve my skills in this area.

Final Thoughts

No matter how big or small the project, there is always something to learn from the work you have done. Use those projects to reflect on what you have done, determine where you can improve, and push yourself to grow. Working with your manager, mentors, and colleagues, you can further understand how you can use your skills in different situations. Remember, your skills are not just technical, but also soft skills!

Again, my five questions when evaluating my work are:

  1. What did you enjoy?
  2. What did you learn?
  3. What could have gone better?
  4. What feedback did I receive?
  5. What are your opportunities for growth?

What questions do you commonly ask yourself as you evaluate your work?

If you would like to read more, check out some of my other articles below!

Why Taking Notes on Your Accomplishments Can Help Your Career was originally published in Towards Data Science on Medium, where people are continuing the conversation by highlighting and responding to this story.