The Art of Separation in Data Science: Balancing Ego and Objectivity

As a data scientist, I’ve learned that our ideas are more than just abstract concepts — they’re a part of us. We pour our hearts and minds into developing solutions, and it’s only natural to feel a sense of pride and ownership over our work. But this close connection between our ideas and our identity can be a double-edged sword.

Learning a Valuable Lesson

I remember the first time I presented a complex machine-learning model to my team. I had spent weeks fine-tuning it, convinced it would revolutionize our approach. When my colleagues started pointing out potential flaws, I felt a knot in my stomach. It wasn’t just my idea being critiqued; it felt like a personal attack. This experience taught me a valuable lesson: the need to separate our ideas from our ego. It’s a challenge that many of us in the data science field face, yet it’s often overlooked amidst the technical hurdles and resource constraints we deal with daily.

Learning to depersonalize feedback is an important first step in navigating the complex landscape of Industry. But even as I grew thicker skin, I faced another daunting reality: the inherent uncertainty of our idea's success in the real world.

No matter how brilliant a concept seems on paper or in our minds, its practical application can yield unexpected results. I’ve had algorithms that performed flawlessly in test environments fail spectacularly when deployed. These moments can be crushing if we tie our self-worth too closely to the success of our ideas. These challenges underscore the importance of dissociating our ideas from our ego. By creating this separation, we protect ourselves from the emotional rollercoaster that often accompanies innovation. More importantly, it allows us to approach our work with greater objectivity and resilience.

My experience at an AI startup, where we used to optimize call center operations, really drove this point home. We were working on a complex problem: predicting agent performance to intelligently pair callers with agents for optimal outcomes. Our existing model used a Posterior Bayesian technique, which had been working quite well for most of our clients.

However, for one particular client, we started noticing unstable agent performance predictions. To investigate, we employed a time series analysis technique to validate the temporal inconsistency of agents’ performance. The results were puzzling: there was little correlation between an agent’s past performance and their future results. This volatility in performance metrics didn’t align with our understanding of human behavior or how call center skilling worked.

Convinced that I had identified a critical flaw in our existing algorithms, I proposed a new ML model. My approach aimed to better correct the difficulty of each call taken, something I thought our current model was failing to account for adequately. I spent a significant amount of time collecting more nuanced features that I thought would better estimate the difficulty score for each interaction. My ego was fully invested in this idea, and I became a vehement supporter of this new approach.

We deployed the new model, and for the first few days, it seemed to be working. However, after the first few days of success, we realized that the results were just as unstable as before. The model, despite its complexity and additional features, wasn’t capturing the underlying issue. This failure hit me hard. I had been so certain of my solution that I started questioning my abilities as a data scientist. My ego, so tightly bound to the success of this idea, took a significant blow.

Weeks later, we discovered that the real problem was far more fundamental than our algorithmic approach. The agent IDs, which we had assumed were unique identifiers for individuals, were being reused at the call center. This meant that what we thought was a single agent’s performance was actually an amalgamation of multiple individuals’ work.

This realization explained the volatility and lack of correlation in performance that both our original approach and my new model had failed to resolve. No matter how sophisticated our algorithms were, they were working with fundamentally flawed data.

This experience taught me a valuable lesson about the dangers of tying my ego to my ideas. If I had critically challenged my own assumptions and approached my new model with the same skepticism I had for the original algorithm, I would have tried to validate it more thoroughly offline. This process would have likely revealed that my idea suffered from the same inaccurate assumption as the original algorithm: that agents could be uniquely identified by their IDs.

Conclusion

By dissociating my ego from my ideas, I could have saved time, resources, and personal distress. More importantly, I might have identified the real issue sooner. This experience reinforced the importance of maintaining a critical and curious mindset, even — or especially — towards our own ideas. It’s a reminder that in data science, as in many fields, our assumptions can be our biggest blind spots, and our ability to question them is often our greatest strength.