About the role
The Data Scientist role consists of turning data into decisions — experimentation, causal analysis, forecasting, the models behind pricing and risk. Where a Data Engineer makes data available and an ML Engineer productionizes models, the Data Scientist answers the questions: what's driving churn, did the feature work, what happens if we change the price. Hire one when decisions are being made on opinion that could be made on evidence.
Monthly rate
$5,000–$7,500/mo
All-in: contract, benefits, equipment, IP
Experience
10+ years typical
Location
Latin America
Argentina · Colombia · Mexico
Timezone
Full US overlap
Fluent English, onboarded in one week
Core stack
AI tools, daily
Verticals seen
What they own — and what they don't
What they own
- Design and analyze experiments — A/B tests that produce answers, not p-hacked noise
- Build the analysis behind pricing, risk, growth, and retention decisions
- Model and forecast: demand, LTV, conversion, capacity
- Separate correlation from causation before the company acts on the wrong one
- Communicate findings so executives act on them — the analysis is only as good as the decision it changes
What they don't — and who does instead
- Build data pipelines — that's a Data Engineer (and without one, your Data Scientist becomes one, expensively)
- Ship models into production systems — that's an ML Engineer
- Build dashboards all day — reporting is a byproduct here, not the job
- Build LLM product features — that's AI Engineering
Who hires this role, and for what
Product companies flying on intuition. Features ship, metrics move, nobody knows why. A Data Scientist installs experimentation so the roadmap learns.
Businesses with pricing or risk on the line. Fintech underwriting, marketplace pricing, insurance risk — domains where a better model is directly worth money.
Scale-ups with growth questions and piles of data. They finally have the data volume for real answers about acquisition, retention, and unit economics — and nobody trained to extract them.
- 01
Experimentation practice. A/B testing infrastructure and methodology, so 'did it work?' has an answer other than a shrug.
- 02
Churn and retention analysis. Who leaves, why, what predicts it, and which intervention actually changes it.
- 03
Pricing and risk models. The quantitative core behind what you charge and what you approve.
- 04
Forecasting. Demand, revenue, capacity — replacing spreadsheet folklore with models that state their uncertainty.
Work our engineers at this role have shipped
- Pricing experimentation program for a US e-commerce brand — quantified elasticity by segment, +6% margin
- Causal analysis of onboarding funnel changes for a mid-market SaaS
- Credit risk model prototype (pre-productionization) for a LATAM digital bank
Do you actually need a Data Scientist?
You do, if:
- Big decisions cite anecdotes because nobody can run the real analysis
- You A/B test but don't quite trust the results
- Pricing, risk, or growth models would move revenue and don't exist
- You have years of data and no one whose job is learning from it
You probably don't, if:
- Your data infrastructure is a mess — hire the Data Engineer first, in that order
- You need dashboards and reporting — a BI analyst is cheaper and fits better
- You need models served in production, not analyzed — that's an ML Engineer
Not sure which role fits? Tell us the problem instead of the title — we'll tell you what we'd actually staff, even if it's not this. If it is this: discovery call today, matched profiles in 48 hours, onboarded in a week.
Hire a Senior Data Scientist