Get Data Scientists
Get Data Scientists from Latin America
Applied data scientists from Brazil, Argentina, and Colombia who run experiments, build predictive models, and communicate findings that lead to decisions. They do more than make charts. They ask the right questions and build systems that answer them.
Why LATAM for data science
Strong cost advantage
Senior data scientists in the US earn $155k-$210k. LATAM equivalents deliver at $68k-$108k, meaningful for roles that require ongoing modeling and analysis work.
Timezone overlap
LATAM's 4-8 hour daily overlap with US teams makes weekly analysis reviews, stakeholder presentations, and collaborative experiment design practical.
Business communication alongside technical depth
Good data scientists translate statistical findings into decisions. LATAM data scientists in our network are comfortable presenting to both technical and non-technical stakeholders.
Strong quantitative academic backgrounds
Brazil and Argentina produce strong statistics, mathematics, and computer science graduates. Several data scientists in our network have graduate-level quantitative training.
Industry domain experience
LATAM data scientists have worked across fintech, e-commerce, logistics, and healthtech, industries where the business context for data science decisions is well understood.
What our data scientists know
Python (Pandas, NumPy, Polars)
Scikit-learn
PyTorch / TensorFlow (deep learning basics)
Matplotlib, Seaborn, Plotly
Jupyter / JupyterHubHow we vet data scientists
Every candidate completes all seven stages before you see their profile. You can also run your own technical round after our screening.
* For common roles like full-stack. Other roles typically take 7 to 10 days.
CV check
Stage 1We review experience, stack, seniority, and career history against the role before anyone is invited to interview. Most applicants stop here.
Behavioral interview
Stage 2A structured interview assessing communication style, conflict resolution, ownership mindset, and English proficiency in a professional context.
Technical interview 1
Role-specificA 90-minute live session covering three areas. First, EDA and hypothesis testing: given a dataset, candidates identify key patterns, state hypotheses, choose appropriate statistical tests, and interpret the results correctly, including effect size, not just p-values. Second, modeling: given a business problem, they choose between models and justify the tradeoff (interpretability vs accuracy, speed vs precision), handle missing data and class imbalance, and select meaningful evaluation metrics for the use case. Third, communication: they present findings from the EDA to a non-technical stakeholder. Clarity, narrative, and avoiding misleading visualizations are all evaluated.
Technical interview 2
Stage 4A second, deeper round on system design, architecture tradeoffs, and code review, matched to the seniority you are hiring for.
Take-home assessment
Stage 5A practical assignment that mirrors real work. We review code quality, testing, documentation, and how the candidate explains their decisions.
Reference check
Stage 6We speak with at least two professional references who worked with the candidate, not personal contacts.
Background check
Stage 7Verification of work history, education, and identity with written consent, aligned with applicable privacy rules including LGPD where relevant.
After our screening, you can optionally run your own technical round before making an offer.
Sample profiles
Who you might hire
Anonymized profiles from our vetted talent pool. Actual candidates may vary.
Mid Data Scientist
São Paulo, Brazil
4+ years experience
Senior Data Scientist
Buenos Aires, Argentina
7+ years experience
Lead Data Scientist
Bogotá, Colombia
10+ years experience
Want to see profiles matched to your specific requirements?
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What to expect
What sets our data scientists apart
Data scientists in our network are rigorous about the difference between correlation and causation, and honest about when a model's predictions can't be trusted. They use AI tools to move faster on the exploratory and routine parts of data work, while keeping their statistical judgment intact.
Pandas AI and LIDA for exploratory data analysis automation: initial dataset profiling, outlier identification, and distribution summaries happen faster so they can focus on the interesting questions.
AI-assisted feature engineering suggestions using AutoML exploration tools. They use these as a starting point and validate the selected features against domain knowledge, not blindly.
A/B test design is rigorous: they calculate sample sizes, randomization checks, and run CUPED or stratification when variance reduction is needed. They don't interpret results before the experiment is complete.
They communicate uncertainty honestly: a 95% confidence interval is presented alongside a point estimate. They don't let stakeholders mistake precision for accuracy.
Findings are documented in reproducible notebooks with clear assumptions, data sources, and conclusions. The analysis can be re-run six months later without starting from scratch.
LATAM vs US data scientist salaries
Junior Data Scientist
Mid Data Scientist
Senior Data Scientist
Lead Data Scientist
FAQ
Common questions
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