Data Science & Analytics
We run structured discovery, experimentation, and classical ML programs—forecasting, scoring, segmentation—with documented baselines and a clear handoff path when you are ready for production MLOps.
Data science that connects insight to shippable models.
Phases
4-phase program
Timeline
often 4–10 weeks by problem complexity
Outcomes
3 target deliverables
Problem framing
Where teams lose leverage
Dashboards and one-off notebooks do not become product features. Teams need reproducible experiments, honest metrics, and decisions on what should graduate to engineering—not endless exploration.
- 1
Stakeholders ask for predictions without agreed success metrics or data readiness.
- 2
Experiments are not versioned, so nobody can reproduce last quarter’s winner.
- 3
Science outputs stall because no one defined the production promotion criteria.
Target outcomes
What this engagement delivers
Problem framing with measurable targets and feasible data scope
Reproducible notebooks or pipelines with frozen datasets and evaluation reports
Recommendation on build vs. buy vs. defer—with effort ranges for productionization
Scope
Deliverables we commit in writing
Exact backlog is tailored in discovery; below is representative of what enterprise buyers typically require for acceptance.
Exploratory analysis and feature ideation on curated or raw data (with governance)
Classical ML: regression, classification, clustering, time-series forecasting
Experiment design, holdouts, and statistical sanity checks—not vanity metrics
Model cards and bias/limitation notes suitable for product and legal review
Bridge plans to our ML engineering lane when you choose to productionize
Program structure
Phased delivery model
Milestones map to artifacts you can review with engineering, security, and finance stakeholders.
Week 1–2
Frame & explore
Hypotheses, metrics, data audit, and feasibility.
Weeks 2–6
Model & evaluate
Iterative experiments with documented baselines.
Week 6–7
Recommend
Go/no-go, risks, and production path if approved.
Week 7+
Optional production
Hand to MLOps or joint build if scoped as follow-on.
Reference view
Logical architecture
Your production topology will reflect your cloud, identity, and data residency choices — this diagram communicates control points and trust boundaries we design around.
Technology
Typical stack (vendor-neutral)
We standardize on primitives your team can operate — and avoid stack-lock where it hurts maintainability after handoff.
Indicative timeline
Discovery through validated prototype: often 4–10 weeks by problem complexity
Final scope depends on your data maturity, integration count, and compliance requirements — all defined in the written SOW.
Get a scoped estimateGovernance
Security and compliance posture
We implement technical controls and documentation suitable for enterprise procurement — not checkbox theater.
Sensitive attributes handled per policy; no training on data you have not approved
Clear documentation of limitations and where the model must not be used
Separation of exploration sandboxes from production data where required
Procurement
Statements of work, change control, and optional penetration-test windows are scoped explicitly. Legal sign-off remains with your counsel.
FAQ
Technical and commercial questions
Data Science & Analytics
Ready to scope this engagement?
Thirty-minute discovery call. Fixed written scope within a week. No open-ended hourly burn.