Dedicated data science team.
Data scientists, data analysts, BI developers and quantitative analysts work only on your questions, as a dedicated data science team. Azendo employs, trains and manages them from its own offices in Chiang Mai and Bangkok, Thailand. Every analysis starts from a decision someone in your business has to make.
What you get and what you pay.
With offshore staffing or staff augmentation, the provider finds a data scientist and the managing is left to you. We do it as a full service. The data scientists are ours: we employ them, assign them to you full time, give them an office and equipment, train and coach them, and run delivery with you every week.
Price examples
Mid-level, full time, everything below included. Junior and senior levels are priced in your proposal. All 27 things included ››
- Data scientist From USD 4,200 per month
- Data analyst From USD 3,100 per month
- BI developer From USD 3,100 per month
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Service
We manage and develop the team
- A service delivery manager who runs the team and answers for delivery
- A 1:1 with our Head of Delivery every two weeks
- Weekly delivery scoring and monthly capacity reports
- A Talent Success Manager for every specialist
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HR
We are the employer
- Recruitment and technical assessment
- Employment contracts
- Salary and payroll
- Tax and social security
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Facilitation
We provide the workplace
- A desk in our own office in Chiang Mai or Bangkok
- A Workplace Experience Manager on site
- Team events through the year
- IT support
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Equipment
We supply the tools
- Laptop and hardware
- Software licences
- LinkedIn Learning access
- Device security and management
One monthly fee. Everything around the data scientist is in it.
You agree a monthly capacity in hours with us. The fee covers the data scientist's salary and every layer around it: the service delivery manager who runs the team, training and development, the workplace, the equipment and the HR behind the employment. It is the same amount every month.
- One agreement and one invoice, with no upfront fee or management surcharge
- Holidays are already priced in, so April and May cost the same
- Capacity moves up or down at the next monthly cycle
Your data science team, tailored down to every detail.
We select every person for your stack, your product and the way your team works. When the right data scientists are not already with us, we headhunt them and test them ourselves before you meet them.
- Roles and seniority
- Stack and domain
- Way of working
- Capacity and start date
Talk to our teamBook a scoping call - 25 Week 1 Sourcing Headhunted for your brief
- 12 Week 2 AI processing and screening Matched to your stack
- 5 Week 3 Technical testing Pass technical testing
- 1 Week 4 Mindset, culture and logic Proposed to you
Any role your roadmap needs.
These four are the data science roles with their own pages. Beyond them we assign specialists across eleven disciplines, on the same agreement and with the same service delivery manager.
What a dedicated data science team works on.
A dedicated data science team proves its value when analysis turns into action: a forecast the planning team uses, a churn model sales can work from, a dashboard leadership trusts. The data scientists we assign recommend the simplest method that answers the question, build it in your environment and explain the result in plain words. When a model has to run every day, it moves to MLOps under the same agreement.
- Analysis and reportingAnswers to specific business questions, with the method and the caveats written down.
- Forecasting and modelsDemand, churn and pricing models in Python, scikit-learn, XGBoost or PyTorch.
- Experiment designA/B tests set up so the result can be trusted before anyone acts on it.
- Dashboards and BIReports and dashboards that show the numbers decision makers use every week.
- EvaluationModels checked against a baseline and against new data before they are used.
- Handoff to productionModels packaged for MLOps when they need to run every day.
Stacks we work in
- Python
- scikit-learn
- PyTorch
- XGBoost
- TensorFlow
- Jupyter
- pandas
- SQL
- R
- Google Colab
- MLflow
- Docker
- MLOps handoff
- FastAPI
- Kubeflow
Matched to what you run today, agreed at scoping.
What a dedicated data science team is.
A dedicated data science team is a group of data scientists and analysts who work on one company's questions, full time: forecasts, models, dashboards and analysis. Every piece of work starts from a decision someone in the business has to make. Azendo employs the team and runs delivery with you.
Where it pays off most
It pays off when decisions wait on analysis, or when the questions come in faster than one analyst can answer them. The same team stays with your data and your business, so each analysis starts from what the last one found.
Who is on the team
- Data scientistAnalysis, modelling and findings
- Data analystReporting your team can act on
- BI developerSemantic models and dashboards
- Quantitative analystPricing, risk and forecasting
- Service delivery managerRuns the team on our side and answers for delivery
- Talent Success managerFor every specialist: a development plan and coaching every month
- Your product ownerSets the priorities and accepts the work
Other ways to build a data science team, compared.
| Dedicated team, Azendo | Staff augmentation | Project outsourcing | |
|---|---|---|---|
| What you buy | Agreed monthly capacity from people who work only on your product | A person's hours, billed by the hour or the month | A defined scope, at a fixed price or on time and materials |
| Who manages the work | A service delivery manager, with you setting the priorities | You do, day to day: Azendo's estimate is four to eight hours a week per specialist | The vendor's project manager |
| Who answers for delivery | Azendo, every week | You do | The vendor, against the agreed scope |
| How long people stay | On your product, and with Azendo for 3.3 years on average | Until the contract ends | Until the project is handed over |
| Best fit | A product that keeps growing, with a roadmap that runs for years | A short gap in a team you already run | One project with a clear end |
Try us, at zero risk.
- No deposit
- No upfront fee
- No commitment
We agree a time to hire with you. If we can't find the right people for your team within it, you walk away with no commitment and nothing to pay.
Get in touch"We don't hand you a data scientist and step back. We build the team around your product, and we stay in it every week."
Mikkel Schmidt, CEO and founder
Questions about a dedicated data science team.
What does a dedicated data science team do?
A dedicated data science team answers your business questions with data full time: analysis, forecasting, models, experiments and dashboards. With Azendo the data scientists and analysts are ours, managed from Chiang Mai and Bangkok, and they work in your environment on the decisions you set.
How much does a dedicated data science team cost?
Data analysts and BI developers start from USD 3,100 per month, and data scientists and quantitative analysts from USD 4,200, at mid-level and full time. The fee covers the analyst and everything around them: delivery management, a Talent Success Manager and training, the office and equipment, HR and payroll. See how the monthly fee is built.
Data analyst or data scientist: which do we need?
A data analyst answers questions from the data you have, in reports and dashboards. A data scientist builds models that predict or classify. Many companies start with an analyst and add a data scientist once the questions need forecasting.
Do we need clean data before a data science team can start?
It helps, and a little goes a long way. The team starts with the data you have, says where it limits the answers, and a dedicated data engineering team can fix the pipelines under the same agreement.
Which tools does the team use?
Python, SQL, pandas, scikit-learn, XGBoost and PyTorch, in Jupyter, Google Colab or your own environment, with the BI tool your business already reads. When a model has to run in production, it is handed over with MLflow and Docker so the MLOps side can take it from there.
How do the results reach the people who decide?
In plain words. Every analysis ends with the answer, how confident the team is in it and what it means for the decision, presented to the people who asked the question. The method and the data behind it are written down, so anyone can check the work later.
Who owns the models and the data?
You do. The work happens in your environment and your repositories, under your access rules, and every model, notebook and dashboard belongs to you. Nothing is copied to systems of ours, and the documentation stays with the work.
Disciplines that join a data science team.
Each discipline joins under the same agreement, works in the same sprints and reports to the same service delivery manager. You add one at the next monthly cycle, when the roadmap calls for it.
Data science team Data scientist, data analyst, bi developer, quantitative analyst. The core of the team. On the team 
AI engineering LLM features, retrieval and evaluation Add to the team → 
Data engineering Pipelines, warehouses and reporting models Add to the team → 
MLOps engineering Deploying and monitoring models in production Add to the team → 
AI automation engineering Agent workflows and internal automation Add to the team → Let's scope your data science team.
Thirty minutes with a service delivery manager. You leave with a clear picture of the team you would need, what it would deliver and when it could start.
- No deposit and no upfront fee
- No commitment until the right people are on your team
- If we can't find them inside the agreed window, you walk away at no cost
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