See the whole system
Find the real constraint, the hand-offs around it, and the consequence of changing one part.
Amit Mohite · Operations & transformation
I solve operational problems.
I move between the big picture and implementation detail—building enough understanding to make a useful decision, testing it quickly, and managing the risks that come with a moving target.
What I bring
Work at the right elevation.
A difficult problem rarely lives at only one level. I look across the operation, people, process, data and implementation—then go deep where the decision needs it.
Learn by building
Prototype, inspect the data, and work close enough to the implementation to replace assumptions with experience.
Choose the risk
Every decision against a moving target carries risk. Make it visible, decide which risk is acceptable, and add the right controls.
Make change hold
A useful solution must survive real users, exceptions, ownership, support and the next change—not only a demonstration.
Selected work
Decisions, not a technology showcase.
Each story begins with the problem, the trade-offs and the evidence. The tools matter, but only in relation to the decision they helped make.
A leakage-aware study testing whether a person's survey history could be compressed into model weights well enough to predict unseen answers.
What it shows: A good experiment should be able to stop an attractive idea. The useful result was not a digital twin; it was a benchmark, a failure diagnostic, and a better next decision.
Turning a statistical weighting method into a repeatable, inspectable workflow for survey data.
What it shows: A focused tool is useful when it makes a specialist method easier to run, inspect and question—not merely when it hides the calculation.
Depth from the work
More than two decades inside operations.
My experience at Kantar is one connected body of work across research operations, change, support, automation, global programs and transformation.
I learned automation from inside high-volume delivery: understanding what experts notice, why exceptions matter, how people adopt change, and what makes a system dependable after launch. Today I bring that operational perspective to data platforms and applied AI.
Mathematics, software technology and later AI/ML study add formal depth. They do not replace the operating experience; they give me more ways to investigate the problems that experience reveals.
More about the journeyIdeas from the work
Thinking in public.
Observations on expert work, AI supervision, context, experiments and the judgment required to make change useful.
A personal reflection on ambition, usefulness, learning, stability, and choosing work that fits the life being built now.
Why useful AI systems begin with how skilled people handle intent, exceptions, validation, and judgment—not with a model choice.
An experiment in synthetic-data generation found that moving one decision from the prompt into code made the output more precise and the workflow faster.
Start a conversation
Bring me a problem that resists the obvious answer.
I am interested in senior operating roles and consulting conversations around operational transformation, intelligent automation, responsible AI adoption, workflow redesign, and moving from proof of concept to dependable capability.