Data Science Strategy
Use-case prioritisation, success metrics, and a roadmap that ties models to revenue, cost, or risk, aligned with your data maturity and compliance constraints.
Data Science Services in Australia
Dev House Australia is a data science company helping Australian startups and enterprises turn messy operational data into models that ship. We deliver Data Science as a Service (DSaaS), from strategy and feature engineering to production ML and monitoring, backed by 14+ years of delivery through Dev Centre House globally and local collaboration across Sydney, Melbourne, Brisbane, and nationwide programmes.
CLIENTS
Recognised by the Best
IFAV HUB
EvryVision
ZAGG
BMW
WrkWrk
SpeakToFile
EI Electronics
MedXNote
Prosperity
Planitas
AEM Ireland
Emere
Careers Portal
Kurd Shopping
Mophie
FindQo.ie
Tenantin.ie
Dialsave
Patrick Ward & Co. Solicitors
Smart Pricer
Sitara Medical Clinic
Noel's Restaurant
R&B Concrete Pumping
Sextherapy.ie
M&I Interiors
Tenoo Restaurant
Boatbookings
Gyst
Partners Logistics
Broker IQ, YAVIA
Fenchurch Legal
Glenveagh Homes
The Matt Haycox Group
Panacea Financial Bank
Primis Bank
Medserv Medical
Scope
Data Science Services We Deliver
We work in iterative sprints so leadership sees validated outcomes before scaling spend, across FinTech, healthcare, retail, manufacturing, and other regulated and high-growth sectors in Australia.
Customer Analytics Solutions
Segmentation, churn, lifetime value, and journey analysis on governed customer data, so marketing and product teams act on evidence, not averages.
Data Science Development
End-to-end builds: feature stores, training pipelines, APIs, and batch scoring, using Python, scikit-learn, PyTorch, or cloud ML services your team can operate.
Data Visualisation Services
Executive and operational views in Power BI, Tableau, or custom dashboards, connected to model outputs and lineage so numbers stay explainable.
Predictive Analytics Solutions
Forecasting, anomaly detection, and recommendation systems with back-testing and drift monitoring, designed for refresh schedules your business can trust.
Maintenance and Optimisation
MLOps support: retraining, performance reviews, and incident response under SLAs, keeping models accurate as markets and data distributions shift.
Cost
What does a data science solution cost?
Dev House Australia scopes data science after a data and use-case review. Because discovery is part of the work, we price in iterative sprints, each with a clear deliverable (validated hypothesis, trained model, or production pipeline) so startups and enterprises can fund the next phase with evidence. Typical factors include:
- Project Complexity
- Project Duration
- Team Size
- Team Composition
- Professional Expertise of Developers
- Cooperation Model
IT Consulting in Action
Browse all Case Studies →-
- E-commerce
- Mobile App Development
- Web Development
- Data Visualisation
- DevOps
- Android
- Kotlin
- iOS
- Swift
- Angular
- Java
- AWS
-
- Real Estate
- Mobile App Development
- Web Development
- React Native
- Angular
Reviews & Testimonials
What Our Clients Say
Clutch Review
FAQs
Q: What industries do you support for data science programmes?
We deliver models and analytics for FinTech, healthcare, retail, manufacturing, logistics, and public-sector programmes in Australia, always aligning data use, consent, and model governance to your regulatory context.
Q: Do you offer Data Science as a Service (DSaaS)?
Yes. DSaaS gives you a flexible engagement model, strategy, experiments, production ML, and monitoring, without building a full in-house data science function upfront. Scope and cadence are agreed in sprints with clear deliverables each phase.
Q: How long does it take to move from raw data to a production-ready analytics model?
Timeline depends on data readiness. With clean, labelled data, a proof-of-concept often lands in 3 to 4 weeks. If you need cleansing, feature pipelines, or orchestration (Airflow, dbt, cloud schedulers), production-grade delivery is typically 2 to 4 months. We run a short data audit first so Australian teams get realistic milestones.
Q: How do you ensure data privacy and security?
Models only run on data you approve. We use encryption, role-based access, segregated environments, and audit-friendly logging, aligned with the Australian Privacy Act and common enterprise standards (including GDPR where applicable). Sensitive fields can be masked or processed in-region per your policy.
Q: Can you integrate data science solutions with our existing systems?
Yes. We expose scores via APIs, warehouses, or event streams and integrate with ERP, CRM, data lakes, and BI tools your teams already use. Delivery is coordinated with your IT or platform squad so releases fit change windows and observability standards.
Get in touch
Tell us about your project and we will respond from our Sydney team, usually within one to two business days. * indicates a required field.
