PHASE 01
Hypothesis
Frame the decision and success metric before touching features.
Qbatch · Data Science
Models that survive contact with production.
Predictive models and experimentation pipelines designed for decisions — with MLOps so accuracy doesn’t decay after launch.
Discuss a modeling problem↑ 23%
Forecast accuracy
vs baseline
−41%
False positives
anomaly models
4.2×
Experiment velocity
weekly cycles
< 2w
Model to staging
typical path
Lifecycle
From hypothesis to retraining loop.
PHASE 02
Model & validate
Baselines, challenger models, and offline metrics that matter.
PHASE 03
Deploy & watch
Shadow mode, canaries, drift alerts, and SLA dashboards.
PHASE 04
Retrain loop
Scheduled retraining with approval gates and rollback paths.
Problem spaces
Where data science moves the needle.
We start from the decision your team needs to make — then choose the simplest model that wins.
Demand & forecasting
Inventory, capacity, and revenue models with seasonality handled.
Customer propensity
Churn, conversion, and LTV models wired into CRM and product.
Risk & anomalies
Fraud, ops outliers, and quality signals with explainability.
Ranking & NLP
Search relevance, classification, and embedding-based systems.
Data science FAQ
Do you only build models or also productionize them?+
Both. We handle feature pipelines, training, evaluation, deployment, monitoring, and retraining loops so models stay useful after launch.
What kinds of problems do you solve?+
Forecasting, churn and propensity, anomaly detection, ranking, NLP classification, computer vision, and optimization problems.
How do you work with our existing data team?+
We embed with your analysts and engineers — sharing notebooks, MLOps practices, and ownership of models once they’re stable.