Product Development & Innovation
How Text-to-SQL is Democratizing Data
Senior Latin American data scientists who frame the question, run the analysis, and defend the result, working your hours.
Companies hire a data scientist from Deverr when the reporting is already fine and the decisions are still guesses. We place senior Latin American data scientists who own question framing, analysis, experiment design, and the models that come out of it.
They join your team directly, in your warehouse and your notebooks, and we employ them in their own country, so payroll, taxes, and compliance are handled without you opening a local entity.
Someone who has had their analysis challenged in a board meeting knows where this breaks. They catch the A/B test stopped early on a good week, the target that leaked into the training data and made the model look excellent offline, and the average that hides two segments moving in opposite directions.
Quality control is the first question almost every team asks. Here is the filter a candidate passes before you see a profile.
Problem-solving, written and verbal communication, planning and self-management, collaboration across multifunctional teams, attention to detail, running several projects at once, and a continuous learning mindset, held to the same standard at every seniority level.
Most of it goes here rather than into the technical round, because even at mid-level we are looking for staff-level habits in how someone plans work and communicates it. For this role that bar does double duty, since an analysis nobody can follow does not get used.
We ask what tools, languages, and methods a candidate uses day to day and how, because a resume listing pandas and scikit-learn tells you nothing without the how.
Technical match is scored from the CV and a verbal walkthrough of real work rather than a whiteboard score, so you hear how someone reasoned through an actual analysis and what they decided to leave out.
Mubeen Ibrahim, Head of Data, Superbalist. Five-star review.
Deverr has done a great job of meeting and exceeding our expectations. What stands out about Deverr is their engineering team's ability to work efficiently without compromising quality.
Deverr built our ingestion and data modeling engine for Shopify data. They built rock-solid infrastructure, were professional, and responsive. True partners in every sense.
We needed a senior full-stack developer fast. Deverr placed someone in under two weeks who was shipping code by day three. Six months later he's one of our core team members.
We couldn't be happier with the full-stack, AI, and WordPress developers Deverr found for us. They saved us a ton of time recruiting and brought in highly experienced developers who fit our culture perfectly, at a fraction of what we'd pay for U.S. developers of the same skill level. 5 stars, I can't recommend them enough.
Alvis.Care started as my garage project, built on Lovable, held together by vision and duct tape. Within a month of bringing on Deverr, they placed Allan as our lead engineer and our code was professionalized and stabilized. Allan took ownership of the team and gave me back time to sell the vision. If you built something real and need it to become a company, this is how you do it.
Deverr is officially the gold standard for scaling a team without the usual headaches. We will never go back to traditional recruiting after seeing how effortless this was.
.JPEG)
Six things that hold on every data science placement we make.
The same seniority costs 30 to 50% less than a US equivalent hire, and one hourly rate covers their salary, benefits, payroll, and local compliance.
Every data scientist we place has done this work somewhere it counted, so you are not funding someone's training.
Candidates are assessed against your actual warehouse, tooling, and role requirements rather than a generic rubric applied to everyone.
A nearshore hire works US hours, so a question gets asked and answered inside the same working day instead of waiting overnight.
The same data scientist frames the question, pulls the data, runs the analysis, and presents the result to whoever has to act on it.
Move the engagement up or down as priorities shift, without renegotiating from scratch each time.
Four steps, and you run your own interviews in the middle of them.
Tell us what the business keeps asking, what data you already hold, and what the role has to own.
You see a short list of vetted candidates, each scored and briefed against your requirements.
You run your own interviews. We coordinate scheduling and answer anything the scorecard raised.
Your data scientist starts in your warehouse, your notebooks, and your Slack, with payroll and compliance already handled.
If the gap on your team is upstream or downstream of the analysis, these are the roles we fill next to it.
Most placements run one to two weeks from first call to first day, covering sourcing, vetting, interviews, and onboarding. Flag an urgent role on the call and we prioritize it.
A data engineer builds and maintains the pipelines that land your data. A data scientist works on what that data means, running the analysis and the experiments the business decides on. Teams with unreliable data usually need the engineer first.
Yes. We handle payroll, taxes, benefits, and compliance in every LatAm country we place in, so you do not need an entity of your own.
Some of it, yes. A data scientist can build and validate the models behind a feature, including scoring, recommendation, and forecasting work. If you need LLM or generative features shipped, that is an AI engineer, and if you need an existing model served and retrained reliably, that is a machine learning engineer. Describe the outcome on the call and we will tell you which of the three to hire.
Common ground includes Python, SQL, pandas, scikit-learn, dbt, Snowflake, and Looker. We match against your real stack.
Fill in the form below and you will go straight to our calendar to pick a time that suits you. Here is what happens next:
%20(1).jpg)
