Practical thinking. Code that holds up.
My background is in data science, but I came up through mobile QA — which means I instinctively hunt for edge cases and broken states. I want things to work reliably in every condition, not just the happy path.
I write code every day — backend and frontend — so I treat data as part of the engineering problem, not a separate discipline. The goal isn't an interesting experiment, it's a solution that holds up under real conditions.
I work with AI regularly, but I approach it the same way as anything else: what's the actual problem, what's the simplest thing that solves it, and will it still be maintainable in a year.
What you can achieve working with me
Scenario-based outcomes driven by practical engineering.
Demand Forecasting
Goal
Optimizing inventory levels and confidently preventing costly stockouts during peak market seasons.
Approach
Deploying a robust machine learning backend that safely captures historical trends and external events.
Outcome
A stable supply chain, lower inventory holding costs, and the confidence that product availability actually matches what customers are looking for.
Dynamic Pricing
Goal
Moving away from rigid, static pricing logic to capture maximum viable market margin throughout the day.
Approach
Building a solid elasticity model that autonomously adjusts based on live demand signals and competitive pricing.
Outcome
Enhanced profitability through automated, well-tested recommendations—matching the precise price with the right buyer context.
Churn Prevention
Goal
Intervening successfully before high-value customers decide to cancel their subscriptions or move to competitors.
Approach
Integrating sensible behavioral analytics directly into the application architecture to flag dropping engagement patterns early.
Outcome
Giving success teams the signal they need to reach out before it's too late — reducing churn and protecting recurring revenue.
Capabilities
End-to-end data science solutions focused on business utility.
ML Systems
Custom machine learning models designed to forecast demand, predict user behavior, and optimize resource allocation. Built for production.
Fullstack Development
Building scalable web applications from end to end—pairing robust backend APIs with responsive, state-of-the-art frontend interfaces.
MLOps & Deployment
Designing the pipelines and infrastructure that keep models running in production — monitoring, retraining triggers and failure handling. Without overengineering.
Let's discuss your data strategy.
Currently accepting new clients for upcoming quarters. Reach out to schedule a brief introductory call.