Hire senior data scientists who answer real business questions

Senior data scientists who design experiments, build models and explain results to decision-makers, working inside your team on your schedule.

By the Ryz Labs team · Updated October 2026

Hiring data scientists through Ryz gets you senior Latin American data scientists who turn data into decisions your business acts on. They are top 1% of the candidates we interview, they embed in your team and tools, and they work within an hour of US time zones.

What our data scientists work on

Our data scientists sit close to product, operations and finance leaders. They work mostly in Python with pandas or Polars, scikit-learn, statsmodels and SQL, and they present findings in notebooks, dashboards and plain written memos. Common projects:

A/B test design and analysis, including power calculations, guardrail metrics and variance reduction with CUPED.
Causal analysis when experiments are not possible: difference-in-differences, synthetic control and propensity methods.
Churn, retention and lifetime value models that feed planning and marketing decisions.
Pricing and promotion analysis, including elasticity estimates and scenario modeling.
Forecasting for revenue, demand or staffing with Prophet, statsmodels or gradient-boosted models, with honest intervals.
Defining metrics and building the analysis layer that AI and ML teams use to judge whether their systems help.

Skills we vet for

Statistics. Hypothesis testing, confidence intervals, multiple comparisons and Bayesian approaches when they fit the question.
Experimentation. Randomization units, sample ratio mismatch, novelty effects and sequential testing.
Causal inference. Confounding, instrumental variables and quasi-experimental designs with clear assumptions.
Modeling. Regression, classification and time series models in scikit-learn and statsmodels, with interpretable outputs.
SQL and data wrangling. Pulling and validating their own data from the warehouse instead of waiting on someone else.
Communication. Writing a one-page answer an executive can act on, including what the data cannot tell you.
Reproducibility. Version-controlled analysis, parameterized notebooks and code that someone else can rerun.

How we vet data scientists

Recruiters source data scientists whose work changed a decision, not only those with good model scores. Our in-house ARC system ranks the pipeline, and candidates go through structured NTRVSTA AI interviews on statistics, experiment design and business reasoning. Recruiters review each candidate before and after the interview and send you a curated shortlist. AI scores are advisory. Humans decide.

Sample interview topics

An A/B test shows a 3% lift in conversion, but the control group has 8% more users than expected. What do you check, and can you trust the result?
Leadership wants to know whether a new onboarding flow reduced churn, and it launched to everyone at once. How would you estimate its effect?
How would you explain a confidence interval and a p-value to a VP who wants a yes or no answer?
Your churn model is accurate but the retention team cannot act on it. How would you change the model or its output?
Design a metric for whether an AI support assistant is actually helping customers, and describe how it could be gamed.

Ways to hire data scientists

Option	Best for	Trade-offs
Freelance marketplace	A one-time analysis or a defined model	Context about your business is lost when the engagement ends, and quality is hard to judge up front.
Staffing or recruiting agency	Sourcing analysts and scientists at volume	Titles vary widely. Screens rarely test statistical reasoning or communication.
In-house recruiting	Leading a permanent analytics function	Long hiring cycles, and interview loads land on your few senior data people.
Ryz Labs staff augmentation	Adding senior data science capacity to product or analytics teams	You own the questions and priorities. Works best with accessible data.
Ryz Labs AI pod team	When the analysis needs to become a production ML or AI system	A dedicated pod with ML, data and backend engineers. Bigger scope, planned together.

Ryz is not the right fit for a management-consulting engagement focused on strategy decks, or if you want published self-serve pricing before speaking with anyone. Hourly, short-term gigs are better served by freelance marketplaces.

Why hire data scientists from Latin America

Data science is a conversation. The first question is rarely the right one, and the useful answer comes after a few rounds with the person who asked. Data scientists working within an hour of your team can join planning meetings, ask follow-ups live and adjust an analysis the same day.

Latin America has strong programs in statistics, economics and applied mathematics, and many senior data scientists have worked with US companies for years. They write clearly in English, which matters in a role where the deliverable is often a written recommendation.

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FAQ

What is the difference between a data scientist and a machine learning engineer?

Data scientists focus on questions, experiments and models that inform decisions. Machine learning engineers focus on putting models into production systems. Many projects need both, and we can staff either or a mix.

Can your data scientists work with our BI tools?

Yes. They work in your warehouse and your tools, whether that is Looker, Tableau, Power BI, Mode or Hex, alongside Python and SQL.

What does hiring a data scientist through Ryz cost?

Custom quote, scoped per team. You get a plan, a price and the names of the people who would do the work before you sign.

How does contracting work across time zones?

You sign one contract with Ryz. Our engineers work with us as independent contractors, and we handle paying them. They keep hours within an hour of US time zones, including New York hours.

Do you only place data scientists in finance?

No. We work with companies across industries, including financial services, retail, logistics, healthcare and software.

Questions we didn't answer? Email info@ryzlabs.com.