ALL ROLES / DATA SCIENTISTS
Hire a data scientist who turns your data into decisions
Forecasts, experiments, churn and pricing models, analysis the business actually uses. One dedicated data scientist from Latin America who works with your team during your hours, not a freelancer on a short engagement.
TRUSTED BY TEAMS WHOSE CLOUDTASK HIRES HAVE STAYED 5 TO 8 YEARS
How it works
How do I hire a data scientist?
You do one thing: interview. We handle everything else.
Post the role
5 minutes. The role, the motion, the tools, the comp band. That is all we need to start.
Review your matches
3 to 5 matches in 48 hours.
Interview and hire
We coordinate interviews. On Managed Staffing, we also handle payroll and compliance across LATAM.
WHY TEAMS TRUST CLOUDTASK
What you get with every hire
One scientist, your questions
A dedicated person who learns your data, your metrics and your business, so each new analysis starts from context instead of from zero.
Same hours as your team
Latin America and the Caribbean overlap the US working day, so results get discussed with product and finance while the decision is still open.
Checked on your methods
Python or R, SQL, the modeling libraries and the kind of problem you describe. We screen each candidate against your brief.
Replacement guarantee
24 months on Managed Staffing, 6 months on Direct Hire. The guarantee covers the case where the fit turns out to be wrong.
WHY CLOUDTASK
Staffing with numbers behind it
10,000+
hires placed
since 2016
85%
still in seat
past 90 days
24 months
replacement guarantee
on Managed Staffing
12
countries
across Latin America and the Caribbean
PRICING MODEL
Managed Staffing.
Managed Staffing is open to every role we place. We find the person, handle payments and compliance, and stay involved after the hire with check-ins, visibility, and escalation when something is off. You get the person. We keep the machine running. For roles that create demand, outbound SDRs, BDRs, demand generation and full-cycle AEs, Managed Staffing runs on a six-month minimum term.
Pay
One all-in monthly rate
Per person. That is the number, and there is nothing added to it.
In the monthly rate
- Sourcing from the CloudTask staffing and recruiting company
- Screening and vetting
- Matching coordination
- Worker payments
- Compliance and employment administration
- Onboarding support at day 0, 7, 30 and 60
- Ongoing check-ins with the hire and with you
- Time and activity tracking, on by default
- Performance escalation
- 24-month replacement guarantee. Replacements not capped.
- Health benefits available on request
- Equipment benefits available on request
Stays with you
- The work itself
- Priorities and direction
- Who you hire
$299 to start your search. One time, not a subscription. It applies to your buyout or toward your first placement.
Start your searchPRICING MODEL
Direct Hire.
Direct Hire is open to every role we place. We find the person, vet them, and hand them over. You employ them, you manage them, you own the relationship from day one. Roles that create demand usually land here, because live call feedback and daily coaching work best coming straight from your sales leader, and there is no minimum term.
Pay
20% to 30%
Of first-year base salary. One time, quoted per role, paid when they accept.
In the fee
- Sourcing from the CloudTask staffing and recruiting company
- Screening and vetting
- Shortlist of matched candidates
- Interview coordination
- Offer support
- 6-month replacement guarantee. One replacement, same job description.
Yours when they accept
- Employment and payroll
- Compliance and employment administration
- Ongoing management
- Time and activity tracking
- Health benefits and equipment benefits
$299 to start your search. One time, not a subscription. It applies to your buyout or toward your first placement.
Start your searchHow CloudTask compares
Run the numbers. We did.
The same role, three ways to fill it.
Swipe to compare
| US Direct Hire | Staffing Agency | ||
|---|---|---|---|
| Time to First Interview | 48 hours | 3 to 6 weeks | 2 to 4 weeks |
| Payroll & Compliance | Included on Managed Staffing | You manage | Included in rate |
| Vetting Process | Human + AI verified | You screen | Recruiter screened |
| Candidate spam | Screened out before you see a profile | Common | Low |
| Tool Verification | Verified per candidate | Self-reported | Rarely verified |
| Free Replacements | 24 months on Managed Staffing, 6 months on Direct Hire | None | Variable |
Testimonials
Trusted by teams that hire with us
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LATAM has been a gold mine for talent. Our CloudTask hires understand our company culture and mission, and they connect with our customers in a way that drives high NPS and CX scores.
David Barrett CEO, Expensify -
At first we were an SF-based team only. Then I met Amir Reiter back in 2018. After visiting Medellín, I knew LATAM would be key to our growth and success.
Tim Zheng CEO, Apollo.io -
After meeting my CX and CS team in Medellín, we immediately loved the culture and the work ethic. As a European-HQ company, we found LATAM to be perfect to service our American clients.
Carl Carell CRO and Co-founder, GetAccept
SKILLS
Top capabilities to look for in a data scientist
A useful data scientist is not the one with the most complex model. It is the one whose work changes a decision.
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Python, R and SQL
Pulling and shaping data with SQL, and analysis and modeling in Python or R with the standard libraries, in code others can rerun.
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Statistics that hold up
Sampling, confidence, bias and the difference between correlation and cause. The basis for every claim the work makes.
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Experiment design
Setting up and reading A/B tests correctly: sample size, the metric that matters, and when a result is not a result.
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Predictive modeling
Forecasting, classification and regression for problems such as churn, demand or lead scoring, with honest evaluation on held out data.
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Framing the question
Turning a vague business request into a question the data can answer, and saying when it cannot.
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Data quality judgment
Spotting missing, duplicated or biased data before it quietly changes the conclusion.
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Explaining results
Clear charts and plain language summaries for people who will act on the result, including what it does not show.
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Getting models into use
Working with engineers so a model runs on real data on a schedule, instead of living in a notebook.
HIRING GUIDE
How to hire data scientists
Hiring a data scientist goes well when you know which questions you want answered and what data you already have. This guide covers what the role does, how it differs from an analyst or a machine learning engineer, when not to hire one, and what to check in the interview.
What does a data scientist do?
A data scientist uses statistics, programming and domain knowledge to answer questions and make predictions from data. Typical work includes forecasting demand, modeling churn, scoring leads, designing and reading experiments, and finding the drivers behind a metric.
The output is not only a model. It is a recommendation the business can act on, with a clear statement of how confident the result is.
Data scientist, data analyst or machine learning engineer?
A data analyst answers questions about what happened, with reports and dashboards. A data scientist goes further, into why it happened and what is likely to happen next, with statistics and models.
A machine learning engineer builds and runs models in production systems. If your main need is shipping models inside your product at scale, look at a machine learning engineer instead.
When should you not hire a data scientist?
When your data is not yet collected, cleaned or in one place. A data scientist will spend most of the time doing data engineering, and a data engineer or an analyst is the better first hire.
It is also the wrong hire when the questions are mostly reporting: what sold, where, and to whom. An analyst will answer those faster and a data scientist will not have the problems the role needs.
Freelance data scientist or a dedicated hire?
A freelancer fits a single, well-defined analysis with a clear end, such as one pricing study or one model to validate.
A dedicated data scientist fits when questions keep arriving and each answer builds on the last. Context about your data and your business is most of the value, and it stays with a person who stays.
What should I check in the interview?
Give a small, real problem with a sample of your data and a business question. Look at how the candidate frames it, what they check before modeling, and how they explain what the result does and does not show.
Then ask about a model or analysis of theirs that turned out to be wrong, and what they changed. A good answer is specific about the cause, not only the fix.
Red flags in data scientist candidates
Modeling before looking. In the interview problem, watch what they do first with your data sample. A candidate who goes straight to a model without checking for missing, duplicated or biased data will let bad data change the conclusion quietly.
Results only on training data. If they describe how well a model performs only on the data it was built with, that is not an honest evaluation. Ask how it did on held out data it had never seen.
Correlation presented as cause. Ask them to explain a driver they found behind a metric. A candidate who does not separate correlation from cause will hand your team recommendations the data does not support.
Never wrong. Ask about a model or analysis of theirs that turned out to be wrong. No example at all, or an answer that names the fix but not the cause, suggests they have not been checking their own work.
No limits on the result. A candidate who presents findings without saying how confident they are, or what the result does not show, will leave your team acting on more than the data supports.
Is AI replacing data scientists?
AI tools speed up code, exploration and first drafts of analysis, and a good data scientist uses them. They do not decide which question matters, whether the data is biased, or whether a result is strong enough to act on.
The role is moving toward judgment: framing problems, checking results and deciding when to use a general purpose AI model and when a simpler model is better.
How much does it cost to hire a data scientist?
It depends on seniority, the methods and tools, the language requirement and the country the person works from, and any single number describes one scenario and calls it a price.
What is worth understanding is the shape. On Managed Staffing it is one all-in monthly rate per person with payments, compliance and replacement cover inside it. On Direct Hire it is a one-time fee and the person goes on your payroll. We quote your number before you interview anyone.
Why hire a data scientist in Latin America?
Overlap. Analysis is only useful while the decision is open, and a data scientist who works your hours can walk product, finance or marketing through the result the same day.
The second reason is communication. Explaining uncertainty in plain language is half of the job, and your data scientist works with your team in English every day. We screen that live on a call.
How does CloudTask hire for this role?
You send the problems, the tools and the hours you need covered. We source against that brief and come back with 3 to 5 matched profiles within 48 hours, each one screened on a live call and checked against the methods and tools you named.
You interview and choose. On Managed Staffing we handle payments, compliance and onboarding and stay involved with check-ins. On Direct Hire the person joins your payroll from day one. Both carry a replacement guarantee: 24 months on Managed Staffing, 6 months on Direct Hire.
FAQ
The fine print, in plain terms.
Should my first data hire be a data scientist?
Can a data scientist work with our existing tools?
How fast can a data scientist start?
What is the $299 for?
Why is there no number on the monthly rate?
Who is the legal employer?
How does tracking work, and can I turn it off?
What happens if the person does not work out?
Is there a platform or subscription fee?
Ready to hire?
3 to 5 matches in 48 hours. Start with a quick role brief to get your shortlist.