Dedicated data science team.
Data scientists and analysts for analysis, models and evaluation, headhunted for your questions and assigned to you full time. You set the priorities. We employ, train and run the team from our own offices in Thailand.
What you get and what you pay.
With offshore staffing or staff augmentation, the provider finds a developer 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 setup 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
Tailored teams down to every detail.
We make sure your team is 100% tailored to your needs and expectations. We headhunt data scientists when needed, so we deliver high-performing teams.
- Roles and seniority
- Stack and domain
- Way of working
- Capacity and start date
- 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 ten disciplines, on the same agreement and with the same service delivery manager.
"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.
Can we choose the seniority level?
Yes. Junior to principal data scientists are available, priced by level, and we recommend a level based on the scope you share.
Do we manage the analysts ourselves?
No, we do. It's staff augmentation delivered as a managed service: our data scientists and analysts are assigned to your questions full time, and we employ, train and manage them. You bring the decisions that need answering.
What if our stack isn't listed?
Tell us what you use. The list shows what we see most often, and we'll confirm the fit for your setup at scoping.
Can we add a second data scientist later?
Yes, at the next monthly cycle.
Who owns the models and code?
You do, from the first commit. All work happens in your repositories.
Where does a data science engagement start?
The most useful work starts from a decision. Our data scientists push for that framing at the start, because it turns analysis into action. A clear statistical method or a simple rules engine answers many questions that arrive framed as machine learning, and your team recommends the simpler route when it fits.
What if our pipelines aren't ready?
Analysis is only as reliable as the pipelines behind it. When those need work, data engineering outsourcing comes first.
What happens when an analysis becomes a daily model?
When an analysis becomes a model that has to run every day, it moves to MLOps within the same agreement. Capacity moves between disciplines at the monthly cycle.
Build the whole team around your data scientists.
Most partners add a second discipline within the first year. It joins the same agreement, the same sprints and the same service delivery manager.