Assign a data scientist to your roadmap.
Data science capacity on your product, month after month — assigned under one service agreement, run by a service delivery manager, with quality gates and reporting you can hold us to. Based in Thailand and overlapping five hours with Northern Europe, the role covers model integration, fine-tuning and evaluation, grounded in the data and outcomes your product actually needs.
One scientist, grounded in your data.
Skills and scope are agreed before delivery starts, so the first sprint is productive rather than a ramp-up month. The work runs to the same data, the same repositories and the same definition of done as your own team’s.
- Model development
From first hypothesis to a validated, production-ready model.
- Fine-tuning and evaluation
Models tuned and benchmarked against your real data, not generic datasets.
- Data analysis
Findings translated into a plan, not just a notebook.
- Sprint planning and stand-ups
In your rhythm, your tools, alongside your product owner.
- Documentation and handover
Methodology and evaluation results documented for the next iteration.
Assigned to fit the stack you already run.
We match the specialist's stack to your data and infrastructure at scoping, not after the contract is signed. The lists below cover the frameworks and tooling we see most often across data science work.
One fixed fee for a monthly average.
We calculate the specialist's working hours across a full year, deduct annual leave and public holidays, then divide by twelve. That average becomes the fixed monthly capacity and price in your agreement, so budgeting for a data scientist stays predictable whether a given month runs light or heavy on hours.
The data science roles we assign.
Take one role, or several as one delivery team. Each role below has its own page describing what it delivers under a service agreement.
Building in-house, a local agency, or Azendo.
Each fits a different situation. An in-house role makes sense when the work is permanent and local; a local agency suits a one-off project with a clear end date. Azendo sits between the two — ongoing capacity for work that keeps coming, with the team, the workplace and the administration behind it handled on our side.
| Comparison | Building it in-house | Local agency | Azendo |
|---|---|---|---|
| Time to productive output | Months — recruit, onboard, ramp up | Fast to start, slow to learn your product | 2–6 weeks |
| Continuity of context | Resets when someone leaves | Rebuilt with each new project | Held by the same delivery team, for years |
| Cover during leave or absence | Your problem to solve | Not applicable, project-priced | Included — fixed fee, unaffected |
| Who answers for delivery | You do | Account manager, between projects | A service delivery manager, continuously |
| Cost profile | Fixed, whatever the workload | Priced per project | One monthly fee, adjustable each cycle |
| Scaling a discipline | A new hire each time | Re-scoped each engagement | Capacity up or down at the monthly cycle |
Questions about this role.
Can I choose the seniority level?
Yes. Junior through principal data scientists are available, priced by level. We'll recommend a level based on the scope you share before delivery starts.
What if the stack isn't listed above?
Tell us what you use. The list above is what we see most often, not a limit — we'll confirm fit for your exact setup at scoping.
Can I add a second data scientist later?
Yes, at the next monthly cycle. Capacity moves with your roadmap rather than locking you into the original scope.
Who owns the models and code the data scientist writes?
You do. Work happens in your repositories, under your license terms, from the first commit.
Often assigned alongside a data scientist.
A specialist rarely works alone on a roadmap. These disciplines cover the ground around the role and can be added to the same service agreement.
Tell us what you are looking for.
Tell us about your project and the capacity you have in mind. A service delivery manager will get back to you.
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