Since 2010
Building Software
Decisions, Backed by Evidence.
Data Science Services
We turn messy data into decisions your teams can defend: analysis, experimentation, and modelling delivered with reproducible pipelines, honest uncertainty, and dashboards built for the people who use them.
- 15+
- Years Building Software
- 9,000+
- Projects Delivered
- 500+
- Engineers
- 25+
- Countries Served
Trusted by the brands we build for
9,000+
Projects Delivered
3,900+
Apps Published
500+
Engineers
insight
What good data science looks like
Good data science answers a decision, not a curiosity. Which customers will churn, which price change pays for itself, which feature actually moved retention, and how confident are we in each answer. We work decision-backwards: define the question, audit the data, then analyse with methods proportionate to the evidence available.
Everything ships reproducible: versioned analysis, documented assumptions, and dashboards your analysts own after handover. When analysis graduates into a production model, it plugs into our machine learning development practice.
What our data science services cover
From one-off deep dives to embedded analytics capability.
Exploratory analysis
Deep dives into your data that surface what drives the metric you care about, with findings documented for repeatability.
- ✓Data quality audits
- ✓Cohort and funnel analysis
- ✓Documented notebooks
- ✓Executive-ready findings
Experimentation and A/B testing
Properly powered experiments with correct analysis, so product decisions rest on evidence rather than the loudest opinion.
- ✓Experiment design
- ✓Power and sample sizing
- ✓Guardrail metrics
- ✓Readable result reports
Predictive modelling
Models that score and forecast outcomes, validated honestly and handed over with documented limitations.
- ✓Feature engineering
- ✓Cross-validated models
- ✓Uncertainty reporting
- ✓Handover documentation
Customer analytics
Segmentation, lifetime value, and retention analysis that marketing and product teams act on directly.
- ✓Behavioural segmentation
- ✓LTV modelling
- ✓Churn driver analysis
- ✓Campaign measurement
Analytics engineering
Reliable data models and transformation pipelines so the numbers in dashboards agree with each other.
- ✓Warehouse modelling
- ✓Transformation frameworks
- ✓Data tests
- ✓Metric definitions
Dashboards and reporting
Decision-grade dashboards built around the questions your team actually asks each week.
- ✓Self-serve dashboards
- ✓Metric definitions
- ✓Automated refreshes
- ✓Alerting on thresholds
What every engagement includes
Every data science project ships with the following, agreed in writing before the first sprint begins.
Discovery workshop
A structured workshop maps your goals, users, and constraints into a scoped data science roadmap with a fixed written quote before any sprint starts.
Named senior team
You get named, senior engineers with CVs you can review, not a rotating bench. The people in the kickoff are the people who ship.
Sprint demos every two weeks
Working builds are demoed live each cycle with CI running on every merge, so progress is visible in software, not slideware.
100% source-code ownership
All data science source, configurations, designs, and documentation transfer to you on delivery with no licensing strings or vendor lock-in.
60 days of post-launch support
Engineering time is on standby for two months after go-live to triage defects, tune performance, and stabilise the release at no extra cost.
12 months of free updates
Compatibility updates, security patches, and platform upgrades ship free for a full year after handover.
What you can build with data science
Work products our clients put into weekly use.
Retention analytics
Cohort analysis and churn-driver findings that show precisely where and why users leave, with confidence intervals.
Experimentation platform
An A/B testing practice with correct statistics, documented metric definitions, and a decision log.
Unit economics model
Acquisition, margin, and lifetime value analysis per segment, so growth spend follows evidence.
Operations dashboard suite
Self-serve dashboards for operations and leadership with agreed metric definitions and automated refreshes.
Forecasting models
Revenue and demand forecasts with uncertainty bands, backtested against history before adoption.
How a data science Project Runs
Six stages, each with a sign-off gate before the next begins.
- 01
Discovery
We map your analytics goals, users, and constraints in a structured workshop, ending in a fixed written quote and an architecture risk list.
✓ step 01 complete - 02
Solution design
The data science architecture, integrations, and delivery plan are designed and reviewed before build work starts, so scaling later is not a rewrite.
✓ step 02 complete - 03
Foundation sprint
Repositories, CI, environments, and a walking skeleton are set up first. Something real runs on a staging URL before feature sprints begin.
✓ step 03 complete - 04
Sprint delivery
Two-week sprints with live demos every cycle. data science work is reviewed, tested, and merged continuously, so scope surprises surface early.
✓ step 04 complete - 05
Hardening and launch
Performance, security, and accessibility checks run against agreed budgets, followed by a staged analytics launch with monitoring from hour one.
✓ step 05 complete - 06
Support and handover
60 days of included support, 12 months of free updates, and documentation that lets your internal team own the system with confidence.
✓ step 06 complete
The stack behind our data science work
Default choices, stated with reasons.
Warehouse and transformation
Modern cloud warehouses with tested, versioned transformation layers, so every metric has one definition in one place.
Analysis tooling
Python-based notebooks and scripts under version control, with environments pinned so analyses reproduce months later.
Experimentation
Sequential testing frameworks with guardrail metrics and pre-registered analyses, protecting against peeking and p-hacking.
Visualisation
BI tooling your team already uses where possible, with metric documentation shipped alongside every dashboard.
How intelligence flows through the work
What the analysis reads, and what it changes.
Data quality layer
Tests freshness, completeness, and consistency before analysis starts.
Reads
Source systems, ingestion logs
Changes
Quality flags and test reports
Analysis layer
Answers defined questions with reproducible, versioned methods.
Reads
Modelled warehouse data
Changes
Findings documents and notebooks
Reporting layer
Serves results to decision-makers with definitions attached.
Reads
Metric tables, analysis outputs
Changes
Dashboards and alerts
What's included, standard
The baseline on every data science engagement. Not an upsell tier, just the floor.
Fixed written quote
The price agreed at discovery is the price you pay, with change handled through explicit scope changes rather than silent drift.
Solution architecture document
System design, data flows, and technology decisions written down so your team can audit and own the result.
Dedicated project manager
One accountable point of contact with real authority over the delivery, not a message relay between time zones.
QA on every build
Automated and manual testing on each data science release candidate, with defects triaged before anything reaches production.
Security review
A checklist-based security pass covering authentication, data handling, and dependency vulnerabilities before launch.
CI/CD and environments
Pipelines, staging, and preview environments configured from week one, so releases are routine rather than dramatic.
Analytics and monitoring
Instrumentation wired before launch so you can see real user behaviour and system health from day one.
Documentation and handover
Code comments, runbooks, and architecture notes written for your next developer rather than for our convenience.
Data audit
An audit of your sources, quality, and metric definitions before analysis begins, with gaps documented.
Embedded data science vs Ad-hoc consulting
How our model differs from report-farming consultancies.
| Criteria | Consideration | Moxace Embedded Model | Typical Ad-Hoc Consulting |
|---|---|---|---|
| Reproducibility | Versioned, documented analysis | Slides and spreadsheets | |
| Metric definitions | One agreed source of truth | Each report redefines them | |
| Team continuity | Same senior team throughout | Rotation after handover | |
| Handover | Your team owns the pipelines | Knowledge leaves with the vendor | |
| Path to production | Direct into our ML practice | Ends at the deck |
/11
Why teams pick us for data science
The differences live in engineering habits, not pitch decks.
Decisions, not deliverables
Every engagement is scoped around a decision you need to make, with the analysis designed to inform it rather than pad a report.
Honest uncertainty
We report confidence intervals and limitations, because a finding without uncertainty is marketing, not science.
Reproducible by default
Versioned analysis, documented assumptions, and tested data models mean results can be checked and rebuilt by anyone.
Bridge to production
When analysis deserves automation, it plugs directly into our ML engineering practice without a vendor handoff.
Track record since 2010
9,000+ projects and 3,900+ apps across 25+ countries, with analytics teams embedded among them.
Data science services FAQs
$ moxace launch --nowReady to Decide on Evidence?
Talk to our data science team about an engagement scoped to a real decision, with reproducible analysis and full handover.
- 01
Share your idea
a quick call, no deck required
- 02
See the live demo
every screen, admin to user
- 03
Launch with source
full code, your brand, your servers
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