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Rivac AI Labs
Research, Academic & Narrative Insight

Statistician

Most in-house "data analysis" quietly skips the parts that make conclusions defensible (proper sampling, a pre-registered hypothesis, controls for confounding) and it shows the moment someone challenges the result. This service brings real statistical method to your specific dataset or planned experiment: hypothesis testing, causal-inference design, power calculations, and sampling strategy, not a dashboard full of correlations dressed up as findings. You'd bring us in when a decision hinges on a result that needs to hold up under scrutiny (a pricing test, a claim in a board report, a finding you plan to publish) or when designing an experiment and wanting it built correctly before spending the budget to run it. The output is either a properly designed study or a rigorous analysis of data you already have, with assumptions, limitations, and confidence levels stated plainly rather than glossed over. It's the difference between a number you can defend in a room full of skeptics and one that falls apart under the first hard question.

This is a capability we deliver for you as a service, not a role we’re hiring for.

How We’d Approach This

A clear, staged plan, not a black box

These are the service-specific steps inside our six-step process.

  1. 1

    Diagnose the actual question being asked, the data or experiment available to answer it, and where the current approach risks a false conclusion.

  2. 2

    Pilot the analysis or experiment design on a smaller sample or a single hypothesis to confirm the method holds before committing full resources.

  3. 3

    Review the results together in plain language (what's statistically solid, what's suggestive, and what isn't supportable) before it goes into any report or decision.

  4. 4

    Deliver the full analysis or experiment design with documented methodology so the result can withstand outside scrutiny.

What You Get

Deliverables from this engagement

  • A written statistical analysis or experiment design with methodology, assumptions, and confidence levels stated explicitly
  • A sampling or power-calculation plan for any planned experiment
  • Reproducible analysis code or a documented calculation trail
  • A plain-language summary translating the statistics into what they mean for the decision at hand

Six Ways We Could Architect This

Different engagement, different build, pick the shape that fits

There’s more than one way to deliver on this service. Browse a few of the ways we’d structure the work, depending on your speed, budget, and integration needs.

Read all 6 architectures as text

Lean Fast-Start Architecture

Intake

  • Business Question Defined
  • Scoping Call
  • Available Data Assessed

Design

  • Hypothesis Formulated
  • Sampling Plan Drafted
  • Power Calculation

Analysis

  • Data Collected or Pulled
  • Statistical Test Run
  • Confidence Levels Documented

Delivery

  • Plain Language Review Call
  • Final Analysis Report
  • Reproducible Calculation Trail

Cloud-Native / Managed Services Architecture

Managed Data Layer

  • Cloud Data Warehouse
  • Managed ETL Pipeline
  • Feature and Metric Store

Analysis Operations

  • Managed Notebook Environment
  • Statistical Modeling Service
  • Version Controlled Analysis Scripts

Synthesis

  • Automated Report Generator
  • Confidence and Assumptions Log

Delivery Layer

  • Client Facing Dashboard
  • Scheduled Report Export
  • Access Controlled Sharing Link
  • Usage Analytics on Report

Event-Driven & Automation-First Architecture

Triggers

  • New Experiment Data Lands
  • Automated Data Validation Job
  • Sample Size Threshold Reached
  • Auto Trigger Interim Analysis

Automation Layer

  • Data Quality Webhook
  • Automated Hypothesis Test Run
  • Significance Threshold Checker

Review Gate

  • Result Statistically Robust
  • Flag for Statistician Review
  • Auto Compiled Result Summary

Delivery

  • Notification to Decision Owner
  • Scheduled Experiment Digest
  • Published Analysis Report

Enterprise-Grade & Compliance-Ready Architecture

Governance Layer

  • Statistical Methodology Standard
  • Pre Registration Log
  • Data Access Control Policy

Secure Data Layer

  • Access Controlled Data Warehouse
  • Role Based Analyst Access
  • Versioned Analysis Repository

Auditable Analysis

  • Hypothesis Test Execution
  • Independent Peer Review
  • Assumptions and Limitations Log

Compliance Delivery

  • Redundant Data Backup
  • Audit Trail Export
  • Signed Off Statistical Report
  • Retention Schedule for Raw Data

AI-Augmented, Human-in-the-Loop Architecture

Data Ingestion

  • Raw Experiment or Observational Data
  • Historical Baseline Datasets

AI Analysis Layer

  • AI Assisted Model Selection Engine
  • Draft Statistical Test Results
  • Draft Causal Inference Suggestions

Human in the Loop

  • Statistician Review Session
  • Methodology Actually Sound
  • Flag Corrections to Model
  • Human Approved Analysis

Delivery

  • Documented Assumptions and Limits
  • Client Facing Report
  • Decision Ready Summary

Integration-Heavy / Ecosystem-Connected Architecture

Client Systems

  • Client Data Warehouse
  • Product Analytics Platform
  • BI and Reporting Tools

Statistical Core

  • Experiment Design and Analysis Engine
  • Hypothesis Test Results
  • Causal Inference Model

Two Way Sync Layer

  • Data Warehouse Write Back
  • BI Dashboard Sync
  • Experimentation Platform
  • Executive Reporting System

Shared Governance

  • Joint Review with Data Team
  • Published Statistical Findings

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Ready to get started?

Tell us what you’re trying to get done and we’ll help you find the highest-leverage place to start, scoped small enough to prove itself before you commit to anything bigger.

Talk to us about Statistician

Most engagements like this start as a $500–$2,500 pilot, see full pricing.

Questions? Book a free call