Complex problems. Intelligent solutions.
Strategy, data, AI, and engineering, from concept to execution.
I help businesses identify opportunities, make better decisions, and build technology that delivers measurable value. With 20+ years across quantitative finance, analytics, AI, and product development, I bring both strategic perspective and hands-on execution.
Senior expertise. Hands-on execution. One accountable partner.
Adaptive Data Services is led by Alex Patino, an MIT-trained engineer with 20+ years of experience across quantitative finance, AI, analytics, and enterprise product development.
Clients work directly with Alex, from identifying the problem through building and delivering the solution. Specialized talent can be brought in as needed, with Alex remaining the primary point of contact.
Flexible engagements. No unnecessary layers. Work directly with Alex from strategy through execution.
Bring me a business problem worth solving.
I'll help you figure out what the right solution looks like, whether the data and technology can support it, and how to get it built.
- Business problem
- Data strategy
- Quantitative models
- Product engineering
- Decisions and outcomes
- PredictDemand, revenue, risk, churn, and outcomes, scored against what actually happened.
- OptimizePricing, allocation, inventory, and portfolios under real constraints and uncertainty.
- AutomateDecisions and workflows that today need a person, an inbox, and a spreadsheet.
- BuildData products and intelligent software that put the model in front of the people who act on it.
Prediction is a data problem.
How I work. A model is only as good as what feeds it. Most teams hire for one half and get stuck on the other, so I do both.
Predictive capabilities
- ForecastingDemand, revenue, risk, and time series, scored against what actually happened.
- Signal researchFind, validate, and combine weak signals into one that holds up out of sample.
- Decision systemsScenario simulation and recommended actions, not just a number on a dashboard.
- Model operationsMonitoring, drift detection, and governance so the model stays honest in production.
Data foundation
- IdentificationWhich data is worth having, where it lives, and what it will cost to get.
- ProcurementVendors, licensing, APIs, partnerships, and collection. Legal and clean.
- Data engineeringPipelines, warehouses, quality checks, and lineage that survive a hand-off.
- GovernanceOwnership, access, and policy, so the data stays usable after the project ends.
Tell me what you're trying to solve.
One email, a short call, and a straight answer on whether I am the right fit.