Revenue growth engineered through data-driven decision systems.
Forecasting, recommendation engines, dynamic pricing and optimization designed to move financial metrics.
Forecasting
Optimization
Dynamic Pricing
Recommendation Systems
Chatbots
Image processing with AI
Guardrails & monitoring by default
Fast baseline → measurable iteration
Use cases
What this work improves
Pricing & Revenue Decisions
Improves pricing and planning decisions using future signals—so growth comes with control.
Forecasting
Dynamic Pricing
Optimization
Personalization & Upsell
Improves ranking and recommendations to lift conversion and basket size.
Recommendation Systems
Optimization
Automation via LLM Assistants
Turns repetitive analysis and support workflows into faster decisions with chat-based tools.
Chatbots
Optimization
Planning & Inventory Decisions
Supports inventory, staffing and capacity planning—reducing stockouts and overstock risk.
Forecasting
Optimization
Vision / Image Intelligence
Extracts signals from images for classification, quality checks or automation workflows.
Image processing with AI
Workflow
How I deliver
Discover
Define the goal, constraints, and the success metric (what “good” means).
Design
Audit the data → set a baseline → build a measurable plan.
Ship
Deliver a pipeline and/or API/dashboard + a clear report + handover.
Monitor
Set alerts, drift checks, and an iteration loop so it stays reliable.
Practical delivery: report for decisions, plus automation when needed.
About
What you can expect
Decision systems, not demos
I build ML that drives action.
Most projects fail at handoff. My default is end-to-end delivery: define the metric, build a baseline, ship something usable (report + pipeline/API/dashboard), then add monitoring so it keeps working.
What you get
Clear output + clear next action.
What you avoid
“Model exists” but nobody uses it.
Principles
Measurable — baselines, metrics, A/B when it matters.
Controlled — guardrails, constraints, safe rollouts.
Practical — ownership, handover, and docs.
If it can't be monitored, it isn't production.
Stack
Python
SQL
XGBoost
CatBoost
Dask
Deliverables
Report · Dashboard · Pipeline · API
Typical engagement
1–3 use cases, shipped end-to-end
Projects
Go to /projects →Contact
Let’s talk
Fastest way
Email for project scope, WhatsApp for quick questions.