Charuchith Ranjit
Solving cases, one structure at a time.
At ZS Associates I owned an analytics MVP that grew into a $3M engagement, and designed a multi-agent system that now informs $50M+ a year in marketing spend.
MBA candidate at Carnegie Mellon Tepper · Looking for summer 2027 internships in product management and tech strategy
I owned an R analytics platform for pharma data scientists. I talked to them before writing the roadmap, what they said changed the plan twice, and the client kept adding scope while the MVP was still being built.
More on thisI designed the knowledge base and agent roles for a 10-agent marketing-mix system, and got the client and AWS to agree on the architecture before AWS built it. Its ROI numbers feed the client's budget model.
More on thisI ran three client teams at the same time, handling staffing, scope, and the client conversations for each.
More on thisBilling for 10,000+ employees was being categorized by hand. I worked with the finance team on a billing ontology, then deployed a GenAI classifier against it, checked against a hand-tagged set before it went live.
More on this
Tap a number to see where it came from.
“In God we trust. All others must bring data.”
Featured work
An agentic pipeline that critiques its own models
A team of AI agents that builds marketing-mix models, checks its own work, and produces the ROI numbers behind $50M+ in yearly spend.
$50M+
annual client spend informed
- Agentic AI
- AI Architecture
An analytics platform that writes its own R
Pharma data scientists ask a question in plain English and the platform writes and runs the R. It started as an MVP and grew into a $3M engagement.
$3M
engagement grown from MVP
- Generative AI
- Platform & Workflow
- Product Ownership
Retiring the spreadsheet a reporting process ran on
Swapped a hand-edited Excel reporting process for a layered data lake on AWS. Monthly reporting got 30% faster for 100+ business users.
30%
faster turnaround
- Data Architecture
- AWS
“Everyone designs who devises courses of action aimed at changing existing situations into preferred ones.”
How I work
01
Talk to users before writing the roadmap
Before I wrote the roadmap for the R platform, I sat down with the data scientists who would use it. What they told me changed two features. The MVP grew into a $3M engagement. Read the case study
02
Put the check where mistakes are cheap
A tool that is slower than writing the code yourself does not get used. So generated R runs without an approval click, and users approve the analysis plan up front instead, where a wrong turn is easy to spot. Read the case study
03
Roll out slowly, earn trust
I launched an AI assistant one business unit at a time instead of all at once. It reached 90%+ monthly active use and won the client's firmwide Innovation Award.
01
Get everyone to agree before the build
I designed the agent setup for a 10-agent marketing-mix system and got the client and AWS to agree on it before AWS started building. It now informs $50M+ in yearly spend. Read the case study
02
Prototype to win the work
With the engagement principal, I prototyped a single source of truth for metric definitions and showed it to several Fortune 500 clients. It led to a two-year engagement.
03
Turn client work into a firm asset
I built a reporting system that cut onboarding-to-first-report time 30% across five client deployments. It was later integrated into a $50M commercial product.
These days I'm an MBA candidate at Carnegie Mellon's Tepper School, concentrating in AI in Business, Business Technologies, and Strategy. The longer version.