Hey there, fellow data scientists! I stumbled upon a Reddit post that resonated with me, and I think it’s worth exploring. The author, a year into their first data science job, is feeling burned out, disillusioned, and plagued by imposter syndrome. They’re wondering if they’re just an ‘API monkey’ – someone who’s more of a technical implementation expert rather than a true data scientist.
The author’s concerns are valid. They’ve been working on large text analysis, making API calls, and building data orchestration pipelines using third-party tools. While they’ve led projects and improved their communication and client skills, they’re not doing much classical data science or rigorous modeling. They’re worried that their work is too focused on API wrangling and lacks technical depth.
I think many of us can relate to these concerns. As data science evolves, it’s essential to reflect on our skills and the value we bring to the table. Are we building valuable skills for the data science market, or are we narrowing ourselves too much? What types of companies or industries might value our unique blend of skills?
For the author, and perhaps for many of us, the answer lies in focusing on learning traditional data science or machine learning skills. We should aim to build a portfolio that showcases our technical abilities and impresses hiring managers. It’s also crucial to maintain a healthy work-life balance and not be afraid to explore new opportunities if our current role isn’t fulfilling.
So, what do you think? Are you feeling like an ‘API monkey’ too? How do you stay motivated and focused on building valuable skills in the data science industry?