Feature Engineering: The Part of Data Science Nobody Automates Well
There is a version of this conversation that stays at the level of frameworks, and a version that deals with what actually happens on a Tuesday afternoon. This is the sec…
Priya Raghavan
Career Services Manager
The short version
There is a version of this conversation that stays at the level of frameworks, and a version that deals with what actually happens on a Tuesday afternoon. This is the second kind.
The second-order effects matter more than the first. A change that improves one metric usually degrades another, and the teams that do this well are the ones who name the counter-metric before they start rather than after someone complains.
Where teams get it wrong
Finally, there is the question of who pays and who benefits. When those are different groups, adoption stalls regardless of how good the design is. Aligning them is unglamorous work and it is usually the work that matters.
Scale changes the answer. What works for a fifty-person team fails at five hundred, not because the idea was wrong but because the coordination cost grew faster than the benefit.
A practical approach
Scale changes the answer. What works for a fifty-person team fails at five hundred, not because the idea was wrong but because the coordination cost grew faster than the benefit.
Evidence beats opinion, but only if the evidence is collected before the decision. Retrospective justification is the most common failure mode, and it is very hard to spot from the inside.
What to do next
Start with the constraint rather than the ambition. In most Indian organisations the binding constraint is not talent or budget — it is the absence of a clear owner. Once a named person carries the outcome, the sequencing question tends to answer itself.
Written by Priya Raghavan, Career Services Manager at iAdmit. Fees, cohort dates and eligibility change between batches — confirm the current position with a counsellor before you decide.