Data ScienceML that leaves the notebook and starts earning.
A model that wins on Kaggle but never ships earns you nothing. We build end-to-end ML — data engineering, feature stores, training, evaluation, deployment — so your models run in production and move real numbers, not slides.
Key performance indicators
Model accuracy vs. baseline improvement
Prediction latency in production
Pipeline reliability & data freshness
Cost per model inference or batch run
Delivery plan
Plan delivery, hit milestones, measure outcomes
Data science engagements start with a data audit and problem framing, then iterate through modeling, validation, and deployment phases.
Milestone-based delivery
Progress you can verify, sprint by sprint
- A working demo every week — not a status deck
- A direct line to the engineers building it
- Scope locked per milestone — no surprise invoices
- 1
Phase 1
Data audit & problem framing
- 2
Phase 2
Feature engineering & model selection
- 3
Phase 3
Training, evaluation & iteration
- 4
Phase 4
MLOps deployment & monitoring
Deliverables
What we hand over
Concrete, verifiable artifacts produced during delivery — quality you can audit, not promises.
Trained & evaluated ML model
Feature engineering pipeline
Model serving API or batch runner
Performance dashboard & drift alerts
What we measure
Expected outcomes
Every engagement is tracked against results you can put in front of your board — not effort, outcomes.
Predictions that drive real decisions, automatically
Hours of manual analysis and reporting, gone
Models that stay accurate — monitored, not forgotten
How we integrate
Engagement blueprint
How our teams plug into yours — from day one.
Core team
- Data scientist
- ML engineer
- Data engineer
- MLOps specialist
Prerequisites
- Defined prediction target & success metrics
- Historical training data accessible
- Infrastructure for model serving agreed
Engagement models
- Fixed-scope model delivery
- Iterative ML sprints
- MLOps retainer
Let's build something extraordinary
Production ML that goes beyond notebooks and drives real business outcomes — built by engineers who deploy, not just experiment.
2000+ vetted engineers · 3 global hubs · 98% client retention
FAQs
Data Science & ML questions
Questions about our process, pricing, or technology? Clear answers to the most common ones.
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