Chapter 1: The Job Search Dilemma

Every graduate faces the daunting task of securing employment, and I was no exception. After leaving my analyst position to pursue a master’s degree in data science, I understood early on the importance of preparing for my job search. I knew I needed to update my CV and mentally prepare for the challenges that awaited me after graduation. However, the pandemic hit at the end of 2019, just as I completed my studies.

In this piece, I’ll share my unique experience of transitioning from graduate to data scientist during an unprecedented time. While my story may differ from the experiences of those who navigate job searches in a post-pandemic world, I believe that the lessons I learned during this period are valuable.

It's essential to recognize that securing a job often involves an element of luck. Each candidate applying for numerous positions will have different outcomes. Fortunately, there are strategies we can all employ to enhance our chances, including: honing interview skills, refining our CVs, and undertaking projects that effectively showcase our capabilities.

My background in analytics and mathematics provided a smoother transition into data science, making it feel less like starting from scratch. With that context, let’s delve into my story and the insights I gained as I navigated my way from graduation to securing a data science role. For those who prefer visual content, check out the video below or skip to the end for a summary of my key takeaways.

Chapter 2: Facing Rejection and Interviews

After graduating, I submitted applications to various companies, ranging from large tech firms to startups. Initially, I believed smaller companies would offer me a better chance, but I faced rejection from all of them. This experience taught me the importance of accepting rejection and allowed me to analyze the reasons behind it. While not every company provides feedback, I learned to move on quickly when they didn’t.

Some interview processes stood out. One company requested a comprehensive take-home project that involved creating an automated script with unit tests. Completing this took nearly a week, only to receive minimal feedback: “It runs fine — great job.” This experience was frustrating, as it felt like I had invested time in what ultimately felt like unpaid labor.

Another memorable interview was with a small startup, where I thought the interaction went well. However, the workspace was cramped, housing only about five people. Ultimately, I was informed I was not selected due to funding constraints, a common issue with startups.

The majority of my interviews were over the phone, and I rarely advanced to in-person interviews. This situation heightened my anxiety; if I couldn’t even succeed in phone interviews, how long would it take to land a job?

I soon realized that the data science interview landscape lacked standardization. Job descriptions varied widely, requiring me to learn a broad range of skills. I began applying for non-data science roles as backups. One recurring requirement was experience with cloud technologies, so I pursued AWS Solutions Architect Certification, which I achieved in March 2020, just before the first lockdown.

Chapter 3: Lockdown and CV Enhancement

The UK entered lockdown at the end of March 2020. By this time, I had completed my certification and was eager to apply for jobs again. However, I feared that opportunities would be scarce, given that many other graduates were in the same boat. Rather than wasting my time as an unemployed graduate, I decided to undertake a significant project for my GitHub profile, which would also help me learn Git.

I chose to tackle my first computer vision project, seeking a challenge that would enhance my coding and machine learning skills. Although a data science project may have been a more prudent choice, I aimed to showcase my abilities and potentially pivot to a computer vision role if needed. This project ultimately led to interviews for machine learning and computer vision positions.

Chapter 4: Renewed Interview Efforts

Once lockdown restrictions eased, I resumed my job applications. I recognized that the best way to prepare for interviews was through practice, so I utilized LinkedIn’s “Easy Apply” feature to apply en masse. Concurrently, I sharpened my Python skills with coding challenges and video tutorials.

I recommend tracking all aspects of your interview process. I utilized Notion to keep notes on each application, including questions asked and stages of the interviews. This created a valuable resource for future reference. I continued to refine my CV and tailored it to specific companies of interest.

Researching interview questions from platforms like Glassdoor proved beneficial, especially for recent interviews at the same company. Many of the data science roles I applied for were actually focused on machine learning and computer vision, revealing the importance of significant projects for graduates like myself.

Chapter 5: Securing the Job Offer

Finally, in August, I received an unexpected job offer. The interview process consisted of a phone screening followed by two additional stages involving team members. The phone interview focused on general questions about my application.

The first stage was a competency-based interview with a recruiter and a team member, while the second involved a take-home project requiring analysis of a dataset. I had a week to complete the project and prepare a presentation. The tools I used included Tableau, Power BI, and Python, with a preference for Power BI.

Simultaneously, I completed online assessments, including personality, verbal, numerical, and logical tests. The process was challenging, especially during the presentation when I faced tough questions from the team lead. I felt I had performed poorly, yet a week later, I received an offer. It was surprising that I was rejected from interviews where I thought I excelled, yet received an offer for one I believed I had stumbled through.

I accepted the offer and began my role as a data scientist. However, I regret not completing other interviews; having additional offers could have provided leverage for negotiation and more options.

Chapter 6: Key Takeaways

Reflecting on my experience as a job-seeking graduate, I’ve learned several key lessons:

• The data science interview landscape is chaotic, with a lack of standardization.
• Expect a lot of rejection; learning to accept it, especially from dream companies, is vital for growth.
• Enhancing your CV with certifications can be beneficial, though it may not always lead to immediate results.
• Engaging in projects can significantly impact your job prospects, provided your CV is strong overall.
• Gaining interview experience is invaluable; practice is key.
• Maintain detailed notes on interviews to build a resource for future reference.
• If you feel you underperformed in an interview, remember that nerves can distort your perception.
• When you receive an offer, consider finishing other interviews to maximize your options.

Finally, it’s completely normal to feel uncertain about your career path. As you progress, clarity will come.

For more insights, feel free to explore my other content on YouTube or subscribe to my newsletter for updates on my journey.