Mapping the risk-tech talent pool in Bengaluru before a single role opened.
We were about to open roles on a hunch. The map told us where the talent actually was — and where it wasn’t. We changed the plan.
The outcome, in numbers.
The situation
The bank had a headcount plan for risk-tech but no ground truth on whether the specific talent existed in Bengaluru at the depth and seniority it needed — or what it would cost and how hard it would be to move.
Committing to a hiring timeline and a compensation budget without that picture is how GCC build plans slip two quarters in.
What we did
We mapped the actual pool, not a market estimate. Working from the exact profiles the team needed, we identified the named population across the relevant companies, then layered on the intelligence that makes a map actionable.
That meant salary distributions by seniority, the attrition timing that tells you when people are reachable, and a shortlist of the specific companies where the density was highest.
- 01 A named-profile map of the risk-tech population against the target roles.
- 02 Salary distributions by seniority so the budget was grounded in reality.
- 03 Attrition-timing signals showing when the pool is actually movable.
- 04 A ranked list of source companies to approach first.
The outcome
In three weeks the bank had 847 named profiles, a defensible compensation range, and the four companies to approach first — a build plan grounded in the real pool instead of an assumption.
The map changed the plan: the data showed one seniority band was thinner than expected, so the bank re-sequenced its intake to hire the scarce layer first rather than discovering the gap mid-ramp.
If a case like this looks like your problem, that's usually a sign.
Send us a brief or just describe what you're dealing with. We'll come back with one observation — even if we don't end up working together.