Data Scientist’s Play & Characters
This is a story of many budding Data Scientists at the start of their career. The article talks about routine characters of any fiction play to help draw comic parallel.
Exposition
At the beginning there was nothing, at the end everything will turn back into nothingness. There are many characters in this act. While some left an impression, others would leave with an experience. Now that we are through with an over-dramatic intro, let’s start the story with a cringe-worthy rhyme.
There are too many, good ones are too few,
Their days are numbered, their demand is huge,
Business sees no value, they are probably not through,
When to stop this paltry act, where to start anew.
Learning — The Path of Peril
Learning is the most amazing and important part of a Data Scientist’s career. Many intentionally make the hard choices with steep learning curve and love discovering their own way forward. However no two Data Scientists are equal and all of them contribute something different than others. The field has already stretched too far and there is a thin ice towards the edge. While the fancy name tag, helps one stretch their wings, it could be restrictive otherwise. It is up to the the individual to choose the path of maximum return. Once chosen, they can sometimes go sideways but the experience remains.
Trick is to know what’s new when its still new!
Mentor — Torch Bearer
At the start of an upcoming Data Scientist’s career, the individual with biggest influence is the Mentor. There are few stages which all mentors take while building the Mentee.
Assessment
Knowing the Mentee’s capabilities from the start helps in designing the best learning experience for them. A mentor knows everyone who walks the path is different. While few run faster, others could be smarter.
Assessment of current capabilities
Hand-holding
Most of the times, higher position of Mentors help them to do better job at the start. To bring out the best in an individual is a work of patience and craft. Many mentors spend a considerable amount of their time shaping up the future. In few teams, there are incentive and honourable motives in others. To become a Data Scientist, the absolute necessity is to learn precisely and with timely recall. Learning more than what is taught happens subconsciously. The first experience of working on a live project has a tremendous impact on the efficiency. (More about first project in a later article)
Learning of applicable tools, techniques & know-hows
Reassessment and the wandering
While the hand-holding continues, mentors assess and curate their Mentee. With this back-propagation, they might learn to adapt themselves and their lessons. There is excessive demand of high number of data scientist and outstanding work pressure. Most of the Mentors would be bound by these worldly constraints and would have to put an early stop. The lessons learnt within this step sprouts the seed of learning sown earlier. Some Data Scientists have to learn their path forward on their own, while being disconnected and taking the next steps on their own.
Growth of technical and behavioural spine
Free Flow
There are few who remain lucky enough to reach their local optima in previous step. These freshly minted novice Data Scientists walk the path with little but timely learning quips from their mentors. The mentors usually assume that these Data Scientists have learnt to trust their instincts and are no longer uninitiated. This is usually the first time a mentor very rarely finds the Mentee breaking the bar set by the mentors.
When one can start actually contributing as individual
Equaliser
While the free flow is no longer the shore, it isn’t the deep blue sea where every man is for himself. Equaliser is the deep blue sea after adequate amount of time has passed. This is the region where Mentors and Mentee are equals. It is the region where both are aware of the capabilities and limitations. This is the golden phase.
When one is ready
Business — Touch of Destiny

Any business unit’s primary objective is to add value back to the business. While every Data Scientist takes pride in learning the most complex technique, adding value back to the organisation with the work is incomparable. An important skill that one needs is ability to explain complex methodologies in lay-man terms. Its important to design the explanation based on the audience. Data Science jargon could be recondite for few while many ML enthusiasts are left gasping for more or worse still fail to appreciate the effort put in. Early inclusion of Data Science learners in these business discussions helps them appreciate the work they do. Every Data Scientist needs to be extra careful with their work as sooner or later someone’s life will depend on the work they do. At the very least their’s would.
The Wise Ones

These could be managers, squadron leaders or anyone with data science teams reporting to them. In many instances, these would be the owner of the money bags and assess the Data Scientists. Due to the dynamic nature of the field, the best ones are the ones who lead from the front. This helps them to identify their team’s reality while keeping them updated. The toughest job they do involves being a Data Scientist among non-Data Scientist contemporaries. There are instances where they fail to successfully acclimatise to Data Science and stay bent up backwards. While they aren’t necessarily outdated, they realise the correct investment only when the situation warrants leaving organisation distraught and teams dissolved. This could lead to team’s mass desertion while their morale takes a hit where they feel left behind their contemporaries in industry.
Disclaimer
This is the least important part of this story (Yes, sarcasm!). Everything written here bears no semblance to any person dead, alive, unborn or engineer. It has as little significance to past as it has with future. Goes without saying, its a myopically distorted convolution of an otherwise spectacular career based on some personal and mostly public observations. Needless to say, the sarcastic author had a cup of afternoon coffee while burning an imagery of his experience and it isn’t intended to touch reader’s soul, if there is any. Its intended for someone mildly drunk or going through a spurt of sugar rush so as to avoid serious consequences.
