Chapter 1: The Evolution of Data Science

Over the past ten years, the professional landscape has witnessed a significant shift. A decade ago, the concept of Data Science was gaining traction, merging statistical analysis with software development to create boundless opportunities. Just a few years back, a search for 'Data Science' on Google would yield results heavily focused on 'machine learning' and 'big data.' Fast forward to now, and the once-promising domain seems to have lost its luster.

While positions in Data Science remain, they have undergone a subtle transformation. The focus has shifted from machine learning to hypothesis testing, and from big data to SQL. These responsibilities have effectively transitioned to a new role within software engineering: the Machine Learning Engineer.

Even setting machine learning aside, there exists a wealth of interpretable statistical learning techniques, such as causal inference. Strong statistical models can delineate causal relationships, providing insights into directionality and influence while managing confounding variables. This is fundamental to the social sciences. Instead of relying solely on machine learning, data scientists should be effectively utilized to extract maximum learning from data.

However, these essential skills are often overlooked in today's data science roles. The Bayesian hierarchical model, for instance, offers immense flexibility and inferential strength, yet the time investment required for proper implementation can range from a day to a week.

Unlike tech giants like Google, many contemporary industry leaders are not focused on uncovering deep insights that require significant time investments. Instead, they prefer to reveal a plethora of insights that lie just beneath the surface, often communicated through SQL.

In the last decade, Product Managers have contributed substantial domain knowledge, while data scientists have brought forth the scientific method and statistical inference. Together, they formed a formidable partnership. Now, however, data scientists are relieved of the need to delve deeply into statistics beyond hypothesis testing in favor of acquiring a robust understanding of product and market dynamics.

This marks the advent of the Product Scientist role.

Chapter 2: The Rise of the Product Scientist

A Product Scientist is equipped with an intimate understanding of their product's value proposition, the economics of their industry, and the various customer segments, similar to how psychologists grasp the human mind and economists comprehend financial systems.

As we look ahead, when encountering job titles such as ‘Data Scientist,’ it is essential to interpret them as ‘Product Scientist.’ You will not be asked complex statistical questions like, "Does gender confound the relationship between age and spending?" Instead, the focus will shift to tasks such as "Sum spending grouped by age and gender," allowing the data to reveal its own stories, irrespective of confounding factors.

For data scientists yearning to apply scientific rigor to their work, this evolution may feel like the demise of an exciting career path. However, another perspective suggests that this change grants Product Scientists a place at the strategic decision-making table. You no longer need an advanced degree in statistics; instead, a blend of drive, product intuition, and SQL proficiency can lead to substantial contributions within some of the world's largest organizations.

Embracing change is crucial, as it is the only constant in this dynamic field.

The first video titled "Is Data Science a Dying Career? Data Science Job Market in 2023" explores the current state of the Data Science job market and its perceived decline, providing insights into the evolving nature of this field.

The second video titled "Is Data Science Dying? (Scope & Job Market in 2024)" delves into the future of Data Science, discussing the scope of opportunities and potential shifts in the job market.