Modeling dataset
Target definition, time cutoffs, features, leakage checks, missing-data treatment, and versioned transformations.
Predictive modelingPrediction matters when it changes a decision.
We develop statistical and machine-learning models for forecasting, classification, risk, and time series. The metric is not chosen for convenience: it represents the cost of errors, the prediction horizon, and how the output enters the workflow.
Decision-based metricError cost shapes evaluation.
Honest validationTime, groups, leakage, and drift.
Appropriate baselineComplex models must earn their place.
Monitored operationPerformance after release.
Applications
Modeling tied to an action.
We begin by identifying who uses the prediction, when it arrives, and what action it can change. This determines the target, horizon, metric, and acceptable complexity.
Forecasting and risk
- Demand, volume, and capacity forecasting
- Time series and scenario analysis
- Risk, probability, and prioritization
- Survival and time-to-event models
- Uncertainty intervals and calibration
Classification and scoring
- Propensity and prioritization models
- Fraud, anomaly, and event detection
- Segmentation and customer behavior
- Clinical and operational decision support
- Validation of existing scores and rules
Deliverables
From baseline to monitored model.
The simplest defensible solution is the starting point. Complexity is added only when it improves the relevant decision.
Baselines and candidates
Operational reference, simple statistical models, and more flexible candidates compared under the same protocol.
Validation report
Out-of-sample metrics, calibration, uncertainty, segment behavior, error analysis, and documented limits.
Operational integration
Batch processing or API, tests, versioning, monitoring signals, and criteria for review or retraining.
Validation
The model faces four comparisons.
The question is not merely which model scores highest, but which decision improves and under what conditions.
Against the reference
The current rule, historical average, last value, or heuristic the model intends to replace.
Out of sample
A split aligned with time, unit, group, and the real arrival pattern of new data.
Across segments
Aggregate performance can hide failures across regions, periods, profiles, or rare classes.
In operation
Latency, coverage, actionability, human review, and the total cost of errors.
Frequently asked questions
Technical choices
Do you use statistical models or machine learning?
We use the family that fits the problem. Interpretability, data volume, stability, nonlinearity, cost, and inference needs matter more than the technique's label.
Can you work with limited data?
Sometimes. The assessment determines whether there is enough information, what complexity is defensible, and whether a simple rule or more data would create greater value.
Can you also put the model into production?
Yes, when engineering is part of the scope. Delivery may be batch processing, an API, or integration with an existing workflow, always with appropriate tests and monitoring.
Which decision needs a forecast?
Share the target, decision frequency, available history, and cost of major errors. That lets us assess feasibility before choosing an algorithm.