What Is A Data Scientist?
A data scientist uses statistics and machine learning to turn raw data into predictions and decisions. That means exploring product and customer data for patterns, designing experiments to test what works, building models that forecast things like churn or demand, and explaining the findings so teams can act on them.
- Also called
- Applied Scientist, Decision Scientist
- Reports to
- Head of Data / VP Analytics
- Works on
- Experiments, predictive models, forecasting
- Measured on
- Model accuracy, business impact
What A Data Scientist Actually Does
- 01 · ExplorationAnalyzes product, customer, and usage data to find patterns, trends, and anomalies worth acting on
- 02 · ExperimentsDesigns controlled experiments, defines success metrics, and evaluates the results with sound statistics
- 03 · ModelingBuilds statistical and machine learning models that predict user behavior, demand, and churn risk
- 04 · ForecastingTurns models into forecasts the business can plan around, and checks them against what actually happens
- 05 · CommunicationTranslates findings into clear recommendations for product, engineering, and leadership
Data Scientist Vs. Similar Roles
| Role | Main focus | Primary metric |
|---|---|---|
| Data Scientist | Building predictive models and running statistical experiments | Model accuracy, business impact |
| Data Analyst | Interpreting data to answer business questions and build reports | Report accuracy, turnaround time |
| Data Engineer | Building and maintaining the pipelines that make data usable | Pipeline reliability, data quality |
| Machine Learning Engineer | Designing, training, and deploying models in production systems | Model accuracy, training throughput |
At smaller companies, one hire often covers both analysis and modeling. Split the roles once the reporting workload and the predictive-modeling workload each justify dedicated ownership.
Skills To Screen For
Interview Questions Worth Asking
Hiring A Data Scientist In Latin America
How Much Of The Workday You’d Share
Bogota, Mexico City, Sao Paulo, and Buenos Aires don’t change their clocks, so the overlap shifts when yours does. Times shown are in your team’s time zone. National English-proficiency averages vary by market too: Argentina ranks in EF’s "High" band globally, while Colombia, Mexico, and Brazil sit in the "Low" band. Screen every candidate’s spoken English directly rather than assuming by country.
What It Costs
| Country | Entry level (1-3 yrs) | Senior (6+ yrs) |
|---|---|---|
| Colombia | $24K/yr | $59K/yr |
| Mexico | $27K/yr | $54K/yr |
| Brazil | $32K/yr | $63K/yr |
| Argentina | $27K/yr | $63K/yr |
The LatAm average above is LatamCent’s own hire-page figure for the top 50% of regional salaries, so it can sit above these market-wide per-country ranges.
Typical time from kickoff to signed offer. LatamCent delivers 3 to 5 candidate profiles in the first 10 days.
Frequently Asked Questions
A data analyst interprets data to answer business questions and build reports, and is measured on report accuracy and turnaround time. A data scientist builds predictive models and runs statistical experiments, and is measured on model accuracy and business impact. The two roles overlap at smaller companies, but a data scientist’s work leans more toward prediction and experimentation.
A data scientist focuses on exploring data, designing experiments, and building models that answer a business question. A machine learning engineer focuses on designing, training, and deploying models in production systems, and is measured on model accuracy and training throughput. At smaller companies, one hire often covers both.
US data scientists average around $161,250 a year. Data scientists in Latin America average around $64,500 a year, about 60% less, saving roughly $96,750 a year per hire.
Most searches run 21 days from kickoff to a signed offer, with 3 to 5 candidate profiles delivered in the first 10 days.
No. A data engineer builds and maintains the pipelines and storage systems that make data usable, and is measured on pipeline reliability and data quality. A data scientist uses that data to build predictive models and run experiments, and depends on the engineer’s pipelines being solid before the modeling work can start.
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