Principles of Data Science
When I started my journey of becoming a Data Scientist, it was the ‘wild-west’. The domain was (even more) nascent and data science teams were often struggling to discover their rhythm of business. Today, both the domain and I have become mature over the course of time. In my journey of self-discovery, an important step that I took was to interview various leaders of data science organizations across multiple companies and condense their thinking into tenets of being a successful data scientist. I have been practicing these principles for a while and have fine-tuned them along the way.
So here are the fundamental principles of being a data scientist that we have collectively unearthed:
Customer Impact Overrides Complexity
Scientists measure the value of their labor only in the scale of customer impact. To them, the complexity of solutions is driven by a genuine necessity for improving customer experience and not by ostentatiousness.
Technically Boundless
Scientists know that boundaries are drawn only by those who have a fixed mindset; expertise is gained through sweat equity and not by authority. They are fearless in diving deep to understand every contributing aspect of the customer experience like Systems, Statistics, Machine Learning, Economics, Finance, User Interface or something more.
Ego and Politics are Enemies of Science
Scientists operate best when there is unrestricted information flow. They gather feedback by presenting their ideas with conviction and without ego. They’re team players who give actionable feedback to improve the customer experience or scientific methodology, never to denigrate.
Respect and Earn Customer Trust
Scientists handle sensitive customer information with the utmost care. They understand that in order to sustain the process of improving customer experience through data, it is crucial to respect and earn the trust customers place in them with their information.
Work Backward with Pragmatism to Disconfirm Beliefs
Scientists seldom fall into the trap of conformational bias. They work backward from a clearly articulated goal, identify the underlying assumptions, and strive to disconfirm their hypothesis with scientifically rigorous methods, all while being pragmatic.
Stories Resound more than Statistics
Scientists are often deployed as the primary sensor in a dynamic landscape. They document their finding in an articulate and timely manner. They understand that anecdotes, intuition, and stories resonate with a broader population more than statistics.
Progress is better Than Perfection
Scientists understand that an imperfect but useful solution is more valuable than a perfect solution that is not being used. They also understand that in a fast-paced business environment, the pursuit of perfection is an empty journey, but a perfect effort produces greatness, excellence, and the extraordinary.
With these principles, one can identify and add value to greenfield opportunities with the magic of data science. Please let me know your thoughts and comments below.
