Hire the best Data scientists in Latin America
400k+
ENGINEERS
14 days
to hire
100+
COVERED
30-50%
US hires
Why hire Data scientists through Revelo?
Rigorously vetted senior developers from Latin America who work in your timezone, ready to contribute from day one.
Interview only the best Data scientists
A shortlist of three to five pre-vetted candidates, hand-picked by in-market recruiters. You decide who to interview, you decide who to hire.
One platform for talent, payroll, taxes and compliance
Your team runs legally across 18 countries in Latin America. Manage your engineers without managing the infrastructure underneath them.
Local recruiting experts invested in your hire
In-market recruiters and account managers cover sourcing, offer strategy, and onboarding. They stay with you until your engineer is up and running.
Your team, your terms
Month-to-month engagements mean you're never locked into headcount you don't need. Scale up for a big push, pull back after launch.
Hire the top 1% of Data scientists in Latin America
Hire vetted senior developers, matched to your stack, your timezone, and your budget.
Hire Data scientists who can deliver this and more
Here's what you get when you hire nearshore Data scientists with Revelo.
Hire Data scientistsRevelo's data scientists cover the full range of applied ML and analytics work, from exploratory analysis to deployed model infrastructure. Here's where they typically engage:
Machine Learning Model Development
Design, train, and validate predictive models for churn, propensity, fraud detection, and demand forecasting. Revelo's data scientists work across the model lifecycle, from feature engineering through hyperparameter tuning and evaluation.
Data Pipeline and Feature Engineering
Build and maintain the data pipelines that feed models and dashboards. This includes ETL design, feature store setup, and keeping data quality high enough that downstream outputs are reliable.
Statistical Analysis and Experimentation
Design and analyze A/B tests, run causal inference studies, and produce the statistical rigor that separates a product decision from a guess. Strong candidates flag when an experiment is underpowered before it runs, not after.
Model Deployment and MLOps
Move models from development into production services with monitoring, versioning, and retraining workflows. Revelo's senior data scientists have shipped models into live systems and can own the full deployment scope alongside your engineering team.
Business Intelligence and Reporting
Build dashboards, define metrics frameworks, and surface insights for product and executive stakeholders. Many teams use this as the entry point before moving into ML, or run it in parallel with model development.
Hire Data scientists in 4 simple steps
Get from "we need someone" to your first day together in weeks, not months.
Tell us what you're building and what kind of Data scientists you need: skills, experience level, team dynamics.
Three to five matched, pre-vetted candidates: identity-checked, skills-tested, human-screened. No wading through hundreds of profiles.
Run your own technical interviews. You decide who to interview and who to hire. Full control, no gatekeeping.
Make the offer. Revelo handles payroll, benefits, taxes, and compliance so you can focus on building. Your engineer ships code from day one.
10+ years making it easier to hire elite nearshore Data scientists
Interview pre-vetted candidates who are fluent in English and work in your timezone.
Start hiring
Why hire Data scientists based in Latin America?
Work synchronously with Data scientists in the same or overlapping US time zones. Real-time collaboration, no async tax.
What are Data scientists?
A data scientist builds the models, pipelines, and analytical frameworks that turn raw business data into decisions a company can act on. They design and train machine learning models, run statistical analyses, and work directly with engineering and product teams to ship data-driven features into production.
Day-to-day, a data scientist writes Python or R, queries large datasets with SQL, builds and validates predictive models, and communicates findings to non-technical stakeholders. The strongest ones know when a complex model is overkill and a simpler statistical approach gets the job done faster.
What separates a strong data scientist from a capable one is production discipline: the ability to take a model from notebook to deployed service, monitor it for drift, and own the outcomes after launch, not just hand off a Jupyter notebook and move on.
Why hire Data scientists?
Data scientists directly affect revenue: better churn models extend customer lifetime value, better recommendation engines lift conversion, and better fraud models cut losses that compound quietly until they're obvious. Companies that staff this function well ship faster, price smarter, and retain more.
The role is genuinely hard to fill in the US. Senior data scientists in competitive markets run $141,000–$220,000 in base salary (Glassdoor, 2026), and hyperscalers and well-funded AI startups have pushed total comp well above that. Mid-market teams get outbid before the first interview.
Engineers based in Latin America close that gap. Through Revelo, you access 400,000+ pre-vetted engineers across 18 countries, get a shortlist in 72 hours, and hire a vetted data scientist in 14 days on average, at 30–50% of the cost of a comparable US hire.
What does it cost to hire Data scientists?
US-based senior data scientists run $141,000–$220,000 in base salary (Glassdoor, 2026), before benefits, equity, recruiter fees, or the compounding cost of a role sitting open for two quarters. Once payroll taxes, benefits, and recruiter fees are added, total cost-to-hire for a senior role in the US commonly runs 20-30% above base salary, pushing well past the base range alone.
Data scientists based in Latin America working for US companies price meaningfully below that band. Using the discipline-level backend developer benchmark as the closest tracked proxy (Revelo Salary Guide, 2025), senior engineers based in Latin America run in the range that anchors the all-in figure below; data science specialists with strong ML backgrounds typically price at the upper end. Data science specialists with strong ML backgrounds typically sit at the upper end of that range. Mid-level engineers run $48,000–$70,000; junior engineers run $36,000–$60,000.
For all-in cost through Revelo's Agent of Record model (compensation plus benefits, payroll handling, and Revelo's fee), senior data scientists price in the $86,000–$129,000 range annually (Revelo Salary Guide, 2025). That's the full number, with no surprise placement fee on top. Visit revelo.com/pricing for a role-specific quote by seniority and country.
| Level | LatAm Compensation Range | US Salary Range (Glassdoor, 2026) |
|---|---|---|
| Junior | $36,000–$60,000 | $80,000–$149,000 |
| Mid-level | $48,000–$70,000 | $96,000–$156,000 |
| Senior | $60,000–$84,000 | $142,000–$220,000 |
Why hire in Latin America?
Latin America produces a deep bench of data science talent. Brazil's university system graduates tens of thousands of engineers and statisticians annually, and cities like São Paulo, Medellín, Buenos Aires, and Monterrey have built active ML and data science communities with strong local conference and open-source cultures.
For this role specifically, timezone overlap matters more than most people account for. Data scientists work tightly with product managers, analysts, and backend engineers. Colombia, Mexico, and Peru sit within one to two hours of US Eastern, which means your data scientist joins standups, reviews model results live, and iterates with your team in real time, not on a 24-hour async loop.
English fluency at the senior level is consistent across major LatAm tech hubs. Engineers who've worked with US companies for years communicate clearly in technical reviews, pull request comments, and stakeholder presentations. The cross-functional fit that a data scientist role demands is there.
How to evaluate Data scientists
Start with problem framing. Give a candidate a real business problem (churn is rising among a specific cohort) and ask them to walk you through how they'd approach it. A strong candidate asks clarifying questions, identifies what data they'd need, and proposes a simple baseline before anything complex. A weak answer jumps straight to deep learning.
Probe their production experience. Ask: "Walk me through a model you shipped. What broke after launch and how did you handle it?" Strong candidates describe monitoring, retraining triggers, and how they communicated model drift to stakeholders. Candidates who've only worked in notebooks struggle to give a concrete answer.
Test statistical fundamentals with a specific scenario, not trivia. Ask them to explain how they'd choose between two models where one has higher accuracy but worse calibration. You're not looking for a textbook answer; you're looking for judgment about what matters in your context.
Finally, give a short take-home or live coding task on a real dataset. The goal is to see how they clean, explore, and communicate findings, not just whether they can fit a model.
Why expertise matters
Why Data Science Wins for Decision-Making at Scale
The core advantage of a mature data science function is that it replaces opinion with evidence at a pace manual analysis can't match. A well-built churn model running in production surfaces at-risk accounts before the account manager notices a behavioral shift. A pricing model calibrated on real transaction data beats static pricing rules on margin without adding headcount to the pricing team. The compounding benefit is that decisions get faster and more accurate in parallel, not one at a time.
Common Use Cases
Churn prediction, customer segmentation, product recommendation engines, fraud detection, demand forecasting, search ranking, dynamic pricing, and NLP-based document classification. Most mid-market teams start with one focused use case, see a measurable return, and expand the function from there.
Companies Shipping Data Science in Production
Spotify uses data science to power its recommendation engine across 600 million tracks. Airbnb built its pricing intelligence on ML models that adjust for hundreds of local market signals. Netflix attributes a significant share of engagement to its recommendation system, which its data science team has iterated on for over a decade. DoorDash uses real-time ML for delivery time estimation and dasher routing. These are large-scale examples, but the same model types (recommendation, forecasting, classification) appear in mid-market products every day.
When Data Science Is the Wrong Choice
If your data isn't clean, labeled, and voluminous enough to train on, a data scientist will spend most of their time as a data engineer. For teams under roughly 50,000 rows of reliable training data on their target problem, classical business intelligence and well-designed dashboards often return more value per dollar than ML. Similarly, if the business doesn't have a clear decision that the model would change, building the model first and asking what to do with it second is an expensive detour.
Benefits of working with Data scientists
Revelo's data scientists cover the full range of applied ML and analytics work, from exploratory analysis to deployed model infrastructure. Here's where they typically engage:
Machine Learning Model Development
Design, train, and validate predictive models for churn, propensity, fraud detection, and demand forecasting. Revelo's data scientists work across the model lifecycle, from feature engineering through hyperparameter tuning and evaluation.
Data Pipeline and Feature Engineering
Build and maintain the data pipelines that feed models and dashboards. This includes ETL design, feature store setup, and keeping data quality high enough that downstream outputs are reliable.
Statistical Analysis and Experimentation
Design and analyze A/B tests, run causal inference studies, and produce the statistical rigor that separates a product decision from a guess. Strong candidates flag when an experiment is underpowered before it runs, not after.
Model Deployment and MLOps
Move models from development into production services with monitoring, versioning, and retraining workflows. Revelo's senior data scientists have shipped models into live systems and can own the full deployment scope alongside your engineering team.
Business Intelligence and Reporting
Build dashboards, define metrics frameworks, and surface insights for product and executive stakeholders. Many teams use this as the entry point before moving into ML, or run it in parallel with model development.
What Is a Data Scientist?
A data scientist builds the models, pipelines, and analytical frameworks that turn raw business data into decisions a company can act on. They design and train machine learning models, run statistical analyses, and work directly with engineering and product teams to ship data-driven features into production.
Day-to-day, a data scientist writes Python or R, queries large datasets with SQL, builds and validates predictive models, and communicates findings to non-technical stakeholders. The strongest ones know when a complex model is overkill and a simpler statistical approach gets the job done faster.
What separates a strong data scientist from a capable one is production discipline: the ability to take a model from notebook to deployed service, monitor it for drift, and own the outcomes after launch, not just hand off a Jupyter notebook and move on.
Why Hire Data Scientists?
Data scientists directly affect revenue: better churn models extend customer lifetime value, better recommendation engines lift conversion, and better fraud models cut losses that compound quietly until they're obvious. Companies that staff this function well ship faster, price smarter, and retain more.
The role is genuinely hard to fill in the US. Senior data scientists in competitive markets run $141,000–$220,000 in base salary (Glassdoor, 2026), and hyperscalers and well-funded AI startups have pushed total comp well above that. Mid-market teams get outbid before the first interview.
Engineers based in Latin America close that gap. Through Revelo, you access 400,000+ pre-vetted engineers across 18 countries, get a shortlist in 72 hours, and hire a vetted data scientist in 14 days on average, at 30–50% of the cost of a comparable US hire.
What Does It Cost to Hire a Data Scientist?
US-based senior data scientists run $141,000–$220,000 in base salary (Glassdoor, 2026), before benefits, equity, recruiter fees, or the compounding cost of a role sitting open for two quarters. Once payroll taxes, benefits, and recruiter fees are added, total cost-to-hire for a senior role in the US commonly runs 20-30% above base salary, pushing well past the base range alone.
Data scientists based in Latin America working for US companies price meaningfully below that band. Using the discipline-level backend developer benchmark as the closest tracked proxy (Revelo Salary Guide, 2025), senior engineers based in Latin America run in the range that anchors the all-in figure below; data science specialists with strong ML backgrounds typically price at the upper end. Data science specialists with strong ML backgrounds typically sit at the upper end of that range. Mid-level engineers run $48,000–$70,000; junior engineers run $36,000–$60,000.
For all-in cost through Revelo's Agent of Record model (compensation plus benefits, payroll handling, and Revelo's fee), senior data scientists price in the $86,000–$129,000 range annually (Revelo Salary Guide, 2025). That's the full number, with no surprise placement fee on top. Visit revelo.com/pricing for a role-specific quote by seniority and country.
| Level | LatAm Compensation Range | US Salary Range (Glassdoor, 2026) |
|---|---|---|
| Junior | $36,000–$60,000 | $80,000–$149,000 |
| Mid-level | $48,000–$70,000 | $96,000–$156,000 |
| Senior | $60,000–$84,000 | $142,000–$220,000 |
Why Hire Data Scientists in Latin America?
Latin America produces a deep bench of data science talent. Brazil's university system graduates tens of thousands of engineers and statisticians annually, and cities like São Paulo, Medellín, Buenos Aires, and Monterrey have built active ML and data science communities with strong local conference and open-source cultures.
For this role specifically, timezone overlap matters more than most people account for. Data scientists work tightly with product managers, analysts, and backend engineers. Colombia, Mexico, and Peru sit within one to two hours of US Eastern, which means your data scientist joins standups, reviews model results live, and iterates with your team in real time, not on a 24-hour async loop.
English fluency at the senior level is consistent across major LatAm tech hubs. Engineers who've worked with US companies for years communicate clearly in technical reviews, pull request comments, and stakeholder presentations. The cross-functional fit that a data scientist role demands is there.
How to Evaluate Data Science Candidates
Start with problem framing. Give a candidate a real business problem (churn is rising among a specific cohort) and ask them to walk you through how they'd approach it. A strong candidate asks clarifying questions, identifies what data they'd need, and proposes a simple baseline before anything complex. A weak answer jumps straight to deep learning.
Probe their production experience. Ask: "Walk me through a model you shipped. What broke after launch and how did you handle it?" Strong candidates describe monitoring, retraining triggers, and how they communicated model drift to stakeholders. Candidates who've only worked in notebooks struggle to give a concrete answer.
Test statistical fundamentals with a specific scenario, not trivia. Ask them to explain how they'd choose between two models where one has higher accuracy but worse calibration. You're not looking for a textbook answer; you're looking for judgment about what matters in your context.
Finally, give a short take-home or live coding task on a real dataset. The goal is to see how they clean, explore, and communicate findings, not just whether they can fit a model.
Why Data Science Expertise Matters
The hiring market for data scientists shifted around 2022 and hasn't come back down. After a wave of AI investment, companies that had never hired a data scientist started competing for the same senior talent that established tech teams had spent years recruiting. Compensation benchmarks moved up sharply, and mid-market companies found themselves priced out of a pool they'd previously been able to reach.
At the same time, the scope of the role expanded. Three years ago, a data scientist built reports and ran A/B tests. Now the expectation includes ML model deployment, feature engineering for real-time systems, and working alongside LLM integrations. The job got harder to staff and harder to define in the same cycle.
For a 100-to-500-person company, leaving a senior data science seat open for a quarter has a compounding cost: roadmap features that depend on a recommendation or ranking model stall, product decisions get made on intuition instead of analysis, and the engineers you do have pick up the modeling work at the expense of their primary scope. The seat's vacancy doesn't sit quietly; it slows other functions down.
How Revelo Vets Data Scientists
Every data scientist in Revelo's network completes a multi-stage screen before a client ever sees their profile. Only the top 2% of applicants pass through to the active placement pool.
The screen runs in stages. First, a profile and AI-assisted review filters for relevant experience, education, and employment history. Candidates who clear that stage move to an English fluency assessment, evaluated for both written and spoken technical communication at a level that works in client standups and code reviews.
The technical stage is specific to data science: candidates complete a hands-on challenge covering statistical reasoning, Python or R proficiency, and ML model design. The challenge is graded by senior engineers, not automated scoring alone. Candidates who score well proceed to a soft-skills and collaboration interview, then a live senior technical review where Revelo's own data-experienced engineers probe depth and judgment.
Deeper technical screening at whatever depth your role requires is available on request. If your team needs a candidate who has shipped production MLOps pipelines or worked with a specific framework, Revelo's recruiting team, based in-market across Latin America, can scope the screen to match.
Data Scientist Technologies
Libraries: scikit-learn, pandas, NumPy, XGBoost, LightGBM
Frameworks: TensorFlow, PyTorch, Keras, Hugging Face Transformers, MLflow
APIs: REST APIs, FastAPI, Flask, Vertex AI API, SageMaker API
Platforms: AWS SageMaker, Google Vertex AI, Databricks, Azure ML, Kubeflow
Databases: PostgreSQL, BigQuery, Snowflake, Redshift, MongoDB
Benefits of Building With Data Science
Why Data Science Wins for Decision-Making at Scale
The core advantage of a mature data science function is that it replaces opinion with evidence at a pace manual analysis can't match. A well-built churn model running in production surfaces at-risk accounts before the account manager notices a behavioral shift. A pricing model calibrated on real transaction data beats static pricing rules on margin without adding headcount to the pricing team. The compounding benefit is that decisions get faster and more accurate in parallel, not one at a time.
Common Use Cases
Churn prediction, customer segmentation, product recommendation engines, fraud detection, demand forecasting, search ranking, dynamic pricing, and NLP-based document classification. Most mid-market teams start with one focused use case, see a measurable return, and expand the function from there.
Companies Shipping Data Science in Production
Spotify uses data science to power its recommendation engine across 600 million tracks. Airbnb built its pricing intelligence on ML models that adjust for hundreds of local market signals. Netflix attributes a significant share of engagement to its recommendation system, which its data science team has iterated on for over a decade. DoorDash uses real-time ML for delivery time estimation and dasher routing. These are large-scale examples, but the same model types (recommendation, forecasting, classification) appear in mid-market products every day.
When Data Science Is the Wrong Choice
If your data isn't clean, labeled, and voluminous enough to train on, a data scientist will spend most of their time as a data engineer. For teams under roughly 50,000 rows of reliable training data on their target problem, classical business intelligence and well-designed dashboards often return more value per dollar than ML. Similarly, if the business doesn't have a clear decision that the model would change, building the model first and asking what to do with it second is an expensive detour.
Frequently asked questions
Everything you need to know about hiring Data scientists through Revelo.
How much does it cost to hire Data scientists through Revelo?
All-in monthly costs run roughly $4,600–$5,600 for junior, $5,800–$7,500 for mid-level, and $7,200–$10,700 for senior developers: engineer compensation, PEO coverage, and Revelo's margin combined. No placement fee, no surprise invoices.
How quickly can I hire Data scientists through Revelo?
You'll see a curated shortlist of matched, pre-vetted candidates within 72 hours, and most companies make a hire within 14 days of sharing their requirements.
What is Revelo's vetting process for Data scientists?
Every candidate is identity-checked, skills-tested, and human-screened: technical assessments matched to their stack, soft-skills and English-fluency interviews, and review by in-market recruiting experts before they ever reach your shortlist.
What engagement models does Revelo offer for Data scientists?
Month-to-month, full-time engagements, with no long-term lock-in. Scale up for a big push or pull back after launch as your roadmap evolves, with a 14-day risk-free trial on every hire.
What happens after I hire Data scientists through Revelo?
Revelo handles payroll, benefits, taxes, and compliance across 18 countries, and your dedicated account manager stays with you through onboarding and beyond. Your engineer ships code from day one.
How quickly can I hire a data scientist through Revelo?
Revelo delivers a shortlist of pre-vetted data scientists in 72 hours from the start of your search. Most clients complete interviews and make an offer within two weeks. The average time to hire is 14 days, which means you're moving a role from open to filled in under a month, including evaluation.
What does a data scientist cost through Revelo?
Senior data scientists based in Latin America working through Revelo run approximately $86,000–$129,000 per year all-in (Revelo Salary Guide, 2025), covering compensation, compliance framework, benefits administration, PTO, and holidays, and Revelo's fee. Mid-level roles run lower. That compares to $141,000–$220,000 in US base salary alone (Glassdoor, 2026). Visit revelo.com/pricing for a current, role-specific figure.
How does Revelo vet data scientists?
Every candidate completes a multi-stage screen covering profile review, English fluency, a data-science-specific technical challenge graded by senior engineers, a soft-skills interview, and a live senior technical review. Only the top 2% of applicants pass through to the active pool, and the network is pre-vetted before your search starts.
What engagement model does Revelo use?
Revelo operates as an Agent of Record (AOR): the client keeps the direct relationship with the engineer, who works as an independent contractor; Revelo structures and administers the engagement (compliant local contracts, invoicing, payment, benefits administration) across 18 LATAM countries, as one vendor. Revelo places data scientists as full-time, dedicated team members on a month-to-month basis, with no long-term contract and no cancellation penalty. The engineer works directly on your team. Revelo handles payroll, tax compliance, benefits, and compliance administration through its own infrastructure across 18 Latin American countries, so your legal and finance teams get a clean, single-vendor answer.
What if the data scientist isn't the right fit?
Every engagement starts with a 14-day risk-free trial. If the fit isn't there within the first 14 days, you pay nothing, and Revelo backfills with a replacement at no additional cost. There are no penalties for a placement that doesn't work out. To start reviewing vetted data scientists for your team, visit Revelo.
Our Data scientists know these tech stacks and more
Our talent is experienced in these libraries, APIs, platforms, frameworks, and databases.
Ready to hire Data scientists?
See a curated shortlist of pre-vetted candidates in 72 hours. Only pay if you hire.