Clean, analysis-ready data
Schema, joins, and quality checks that survive handoffs. Reproducible pipelines so tomorrow’s refresh matches today’s results.
Data Science & Statistical Analysis
From messy to meaningful—clean data, validated models, and executive-ready insight. No jargon, just decisions.
Request a Data Science ConsultationWhat you get
Validated, interpretable models
Effect sizes, uncertainty, and assumptions surfaced for leaders. No black boxes—unless requested, with guardrails.
Decision-grade stories
One-page briefs with thresholds and “what to do next,” plus a technical appendix your analysts will love.
Speed to value
Week-one signal using lightweight ingestion and templated visuals. Iterate into depth as the ROI is proven.
Flagship proof asset
See how PrimeStata turned fragmented reporting, inconsistent metric definitions, and unclear executive signals into decision-grade analytics for a multi-region services platform.
Capabilities
Data Wrangling & QA
Data cleanup, joins, QA rules, audit trails, and dictionaries that make reporting and modeling trustworthy.
See our process →Measurement & Scaling
Survey and assessment measurement, score design, norms, and fairness checks when decisions depend on defensible measurement.
Methods →Modeling & Inference
Regression, forecasting, causal analysis, and predictive pipelines used to answer the decision at hand, not just generate output.
Methods →Experimental Design
Experiment design, power planning, guardrails, and readouts that make tests useful to decision-makers.
Case snapshots →Delivery & Enablement
Executive briefs, reproducible notebooks, and lightweight dashboards so teams can use the work after delivery.
Tooling →Methods, explained clearly
Factor Analysis (EFA/CFA)
Clarifies whether a survey or assessment is measuring the dimensions it claims to measure.
IRT & DIF
Shows how items perform across groups and flags bias before scores are used in real decisions.
Regression (Hierarchical/Logistic)
Quantifies what is driving an outcome and how strongly, while accounting for context such as teams, regions, or segments.
Johnson–Neyman & Breslow–Day
Tests whether results hold across ranges and cohorts so rollouts are less likely to misfire.
SEM
Links related drivers and outcomes in one model when leaders need a coherent explanation, not isolated statistics.
Predictive Modeling
Supports next-best-action decisions with transparent thresholds, tradeoffs, and review points.
Tooling
Languages & Environments
R, Python, SQL, jamovi, and related tools chosen for the engagement. Work ships in reproducible scripts and parameterized reports.
Artifacts
Executive one-pager, technical appendix, data dictionary, code-as-deliverable, and action guide.
Governance
Versioned features, model cards, privacy reviews, and decision logs for audit-ready operations.
Access
Secure file exchange or temporary connectors. No production changes are required to begin discovery.
Engagement Options
Diagnostic
Data profile plus quick wins. We audit quality, build a minimal pipeline, and return a one-page “opportunities map.”
Discuss ScopeAnalysis Sprint
Answer one to two priority questions with validated models and a short deck leaders can act on immediately.
Discuss ScopeFull Stack
End-to-end pipelines, dashboards, and enablement with governance. Ongoing iteration and review cadence.
Discuss ScopeExample outcomes
Flagship case: fragmented reporting → decision-grade analytics
A multi-region services platform moved from conflicting spreadsheets and unstable KPIs to validated models, executive-ready outputs, and a reusable analytical foundation for operating reviews.
Review the Case Study →Selection Fairness
IRT with DIF flagged three items; replacements removed adverse impact while preserving predictive validity.
Revenue Uplift
Hierarchical regression linked product signals to expansion; decision thresholds drove a 7–10% lift in target accounts.
How we work
01. Intake
Clarify decisions, success criteria, constraints, and timelines. Identify minimal inputs to get signal quickly.
02. Clean & Document
Profile, stitch, and standardize data. Log assumptions. Produce a dictionary and refreshable pipeline.
03. Model & Validate
Fit interpretable models, surface effect sizes and uncertainty, and run robustness and fairness checks.
04. Ship & Enable
Executive brief plus appendix, optional dashboard, and an action plan with thresholds and owners.