I build the AIsystems peopleactually keepusing.
Nine years of production data science. From marketing science for global brands to the AI infrastructure other teams build on.
A model nobodyuses is a modelthat did not happen.
I started in consulting, so I learned that early. Most of my career has been spent in the gap between "the notebook works" and "the business changed what it does."
Nine years · Four companies · Five industries
~0%
Search precision
More relevant results for AI agents, inside a sub-millisecond budget. No added latency.
−0%
Manual analysis
Weeks of reading customer reviews replaced by a ranked list of what to fix, with evidence attached.
40→40%
Customer churn
Measured against a permanent untreated holdout, on a base of five million customers.
Selected work
Three layers,one discipline.
One from each layer I have worked at: the infrastructure other teams build on, the product a customer touches, and the decision a business makes with money.
01
Nebius: better answers, same speed
AI search relevance. The filtering layer that decides which search results are worth putting in front of an AI assistant.
~29% more precise
no added latency
Read the case →
02
Momos: review replies in minutes
Automated customer replies. An AI that writes on-brand answers to public reviews, and reliably refuses to touch the sensitive ones.
routine cases handled
without a person
Read the case →
03
Yum! Brands: pricing backed by evidence
Menu pricing and promotions. The models and the simulator behind pricing decisions for Pizza Hut and KFC in three markets.
Pizza Hut & KFC
Canada, Brazil, UK
Read the case →
How I work
Three rules I donot bend.
They exist because each one has cost me something to learn. They are also the fastest way to tell whether a project is real.
Agree upfront on what proves the idea wrong.
Written down before any modelling starts. It is the cheapest way to kill a bad project early.
Never ship without an evaluation.
If there is no way to tell whether the output is good, there is no way to tell whether it got worse.
Give stakeholders something they can poke at.
A simulator beats a slide. People trust a number they were allowed to argue with.
Background
Marketing science,then AI product,then infrastructure.
Founders and C-level stakeholders in every role. Solo on the AI work, sole technical lead in the marketing-science years.
Oct 2025 – May 2026
AI infrastructure
Nebius (acq. Tavily)Lead Data Scientist · Abu Dhabi
The layer other teams build agents on: search relevance inside a sub-millisecond budget, a versioned revenue platform at a million records a day, and twelve-month forecasting with scenario simulation.
May 2022 – Sep 2025
AI product
MomosLead Data Scientist (AI) · Abu Dhabi
Took LLM products from prototype to production for multiple brand clients: a guarded RAG response agent, AI Insights, and the LLM stack migration behind both - designed and built solo, end to end.
Aug 2017 – May 2022
Marketing science
Yum! Brands & KvantumLead / Senior Data Scientist · Delhi
Pricing and promotion analytics for Pizza Hut and KFC across Canada, Brazil and the UK, reporting into the Chief Analytics Officer. Before that, four years of consulting on marketing mix modelling, attribution and retention for brands across CPG, retail and pharma.
Notable brands
The names on the other side of the work.
Some I worked with directly on pricing and marketing science. Others I reached through the AI products I built, where the brand count runs into the hundreds.
Reynolds
QSR and CPG direct · several hundred more reached through product at Momos
Toolkit
What I reach for.
Applied AI & ML
LLMs, RAG, agentic systems, prompt engineering, forecasting, segmentation, uplift modelling, churn, price elasticity, marketing mix modelling, XGBoost, CatBoost, neural networks, SHAP
Data engineering
Python, SQL, PySpark, Snowflake, BigQuery, Databricks, Airflow, AWS Lambda, Azure Data Factory, Docker, FastAPI, CDC, SCD2, Tableau, Power BI, Omni
Search & measurement
BM25, hybrid retrieval, ranking and reranking, Elasticsearch, pgvector, evaluation harnesses, A/B testing, convex optimisation, simulation, Kalman filters
Education
Post Graduate Diploma, Applied StatisticsIGNOU, Delhi
B.Tech, Computer Science EngineeringGGSIPU, Delhi
Certifications
Nebius AI Leader CertificationNebius Academy
AI Agents in LangGraphDeepLearning.AI
Building & Evaluating Data AgentsDeepLearning.AI
Contact
Hiring for data,ML, or AI systems?
Immediately available, based in Dubai, and I reply fast. Happy to walk through any case in detail - the decisions, the trade-offs, and what I would do differently today.