Get Data Scientists
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.
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.
LATAM's 4-8 hour daily overlap with US teams makes weekly analysis reviews, stakeholder presentations, and collaborative experiment design practical.
Good data scientists translate statistical findings into decisions. LATAM data scientists in our network are comfortable presenting to both technical and non-technical stakeholders.
Brazil and Argentina produce strong statistics, mathematics, and computer science graduates. Several data scientists in our network have graduate-level quantitative training.
LATAM data scientists have worked across fintech, e-commerce, logistics, and healthtech — industries where the business context for data science decisions is well understood.
Every candidate completes all five stages before you see their profile. You can also run your own technical round after our screening.
A timed test measuring analytical thinking, pattern recognition, and problem-solving clarity, independent of specific programming language knowledge.
A structured interview assessing communication style, conflict resolution, ownership mindset, and English proficiency in a professional context.
A 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.
Verification of work history, education, and identity with written consent, aligned with applicable privacy rules including LGPD where relevant.
We speak with at least two professional references who worked with the candidate in an engineering context, not personal contacts.
After our screening, you can optionally run your own technical round before making an offer.
Sample profiles
Anonymized profiles from our vetted talent pool. Actual candidates may vary.
São Paulo, Brazil
4+ years experience
Buenos Aires, Argentina
7+ years experience
Bogotá, Colombia
10+ years experience
Want to see profiles matched to your specific requirements?
Request matched profilesWhat to expect
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.
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FAQ
Related roles
Tell us your data maturity and what decisions you want models to support. We'll have a shortlist in 3 days.
Python (Pandas, NumPy, Polars)
Scikit-learn
PyTorch / TensorFlow (deep learning basics)
Matplotlib, Seaborn, Plotly
Jupyter / JupyterHub