Job description template

Quantitative Analyst

The Quantitative Analyst will turn complex data into clear, decision-ready insights that improve forecasting, experimentation, and business performance. You’ll build and validate analysis pipelines, collaborate with cross-functional partners, and communicate findings through actionable recommendations.

Responsibilities

Develop and maintain quantitative models to support forecasting, performance measurement, and key business decisions
Design and analyze experiments (e.g., A/B tests), including metrics definition, statistical power planning, and result interpretation
Build analysis datasets and data quality checks to ensure trustworthy inputs for reporting and modeling
Create dashboards and reporting that translate metrics into clear operational and strategic insights
Partner with Product, Marketing, Finance, or Operations to define measurement plans and interpret drivers of performance
Document analytical methods, assumptions, and limitations so results can be reproduced and audited
Present findings to non-technical and technical stakeholders, recommending specific next steps based on evidence

Must-have qualifications

Hands-on experience building statistical analyses and experiments, including hypothesis testing and A/B test interpretation
Proficiency in Python for data analysis and modeling (e.g., pandas, numpy, stats libraries)
Strong SQL skills to extract, transform, and validate data from relational systems
Experience with data visualization and communicating quantitative results to stakeholders
Working knowledge of model validation practices (assumption checks, backtesting, sensitivity analysis, or equivalent)
Ability to independently deliver analysis on well-defined problems with clear documentation and reproducible methods

Nice to have

Experience with time-series forecasting methods and evaluation
Familiarity with causal inference concepts beyond standard A/B testing
Experience using BI tools (e.g., Tableau, Looker) or building metrics layers
Experience working with large datasets and performance-aware data transformations

What success looks like in 12 months

Reliable forecasting and metric modeling
High-quality experimentation and decisioning
Trustworthy analytical datasets
Actionable reporting and insight delivery
Reproducible, auditable analysis workflows

Logistics

Full-time (40 hours a week), remote from Latin America.
Working hours overlap with US business hours.

About the company

[About your company: what you build, your stage and team size]

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Interview scorecard

Score every candidate against the same bar

Deliver decision-ready quantitative insights by building reliable analyses, models, and measurement frameworks that improve business outcomes.

Outcomes to measure

What this hire should deliver in the first year. Replace [N] with your own targets.

Reliable forecasting and metric modeling. Measure: reduce forecast error by [X]% and document model assumptions and validation checks for [N] key models
High-quality experimentation and decisioning. Measure: increase experiment decisions supported by correct statistical analysis to [X]% of experiments and achieve statistical analysis sign-off for [N] tests
Trustworthy analytical datasets. Measure: improve data quality by reducing analysis-impacting data issues by [X]% and implement automated checks for [N] critical data sources
Actionable reporting and insight delivery. Measure: deliver [N] stakeholder-ready reports/dashboards with documented methodology and achieve [X]% stakeholder satisfaction in post-review feedback
Reproducible, auditable analysis workflows. Measure: increase analyses with version-controlled, reproducible notebooks/scripts to [X]% and complete method documentation for [N] recurring workflows

Competencies and weights

What to evaluate, how much it counts and what a strong answer looks like.

Experimentation Statistics · 24%
This role must correctly design, analyze, and interpret experiments to drive reliable decisions. Evaluated in Round 1.

Weak: Explains A/B results without checking assumptions or clarifying metric definitions; misinterprets significance vs. practical impact.
Meets the bar: Defines clear hypotheses and primary metrics; interprets p-values/effect sizes appropriately and explains limitations.
Strong: Plans experiments with appropriate power assumptions; validates requirements (e.g., randomization, data integrity) and communicates both statistical and business implications.

SQL Data Extraction · 18%
The analyst needs to reliably retrieve and transform data to support modeling and reporting. Evaluated in Round 1.

Weak: Uses overly broad queries that miss required filters or joins; produces outputs that are difficult to verify.
Meets the bar: Writes correct SQL with clear joins/filters; performs basic sanity checks and handles missing/duplicated data.
Strong: Builds efficient, well-structured queries; includes validation logic (row counts, reconciliation checks) and manages edge cases confidently.

Python Analysis Modeling · 18%
Python is central for analysis automation, modeling, and reproducible workflows. Evaluated in Round 2.

Weak: Relies on manual steps; code is hard to reproduce or validate; limited use of appropriate libraries.
Meets the bar: Implements analysis pipelines in Python with clean data transformations and reproducible outputs.
Strong: Builds robust, modular analysis code with thorough data handling, clear abstractions, and reproducibility (e.g., consistent inputs/outputs).

Model Validation Rigor · 14%
Sound validation prevents incorrect conclusions and improves forecasting and model credibility. Evaluated in Round 2.

Weak: Applies a model without checking assumptions or failure modes; cannot explain how performance was evaluated.
Meets the bar: Performs validation (e.g., backtesting or holdout) and checks assumptions relevant to the approach.
Strong: Designs validation plans that test sensitivity and robustness; explains trade-offs and failure modes with evidence.

Data Quality and Integrity · 10%
Trustworthy inputs are required for reliable insights and stakeholder confidence. Evaluated in Round 3.

Weak: Inspects data only after issues arise; lacks systematic checks for schema or distribution changes.
Meets the bar: Implements or applies data quality checks and documents known data limitations.
Strong: Proactively creates automated integrity checks, monitors drift/outliers, and reconciles discrepancies across sources.

Stakeholder Communication · 10%
The role requires translating quantitative findings into actionable guidance for varied audiences. Evaluated in Round 3.

Weak: Overloads stakeholders with formulas or misses the decision context; recommendations are not tied to evidence.
Meets the bar: Presents clear findings with supporting metrics and explains implications and limitations.
Strong: Tailors communication to the audience; frames decisions, quantifies trade-offs, and recommends next actions grounded in analysis.

Independent Delivery and Documentation · 6%
Mid-level independence is required to deliver well-defined work end-to-end with reproducibility. Evaluated in Round 2.

Weak: Needs frequent clarification or leaves analysis undocumented; cannot reproduce results reliably.
Meets the bar: Scopes work appropriately for well-defined problems and maintains clear documentation.
Strong: Drives work from ambiguous inputs to decision-ready outputs; documents assumptions, methods, and reproducibility so others can audit quickly.

Interview questions

Questions for every interview round

Each round has an owner, a time box and questions that test the competencies above.

Round 1: Data & Experiment Foundations
Senior Analyst · 55 min

Walk through how you would define primary and secondary metrics for an A/B test and choose the unit of analysis.
Explain how you interpret an A/B test result when statistical significance conflicts with business impact; what would you do next?
Given a messy relational dataset, how would you use SQL to extract the correct analysis-ready table (joins, filters, handling duplicates/missing values) and what sanity checks would you run?
Describe a time you debugged an experiment analysis caused by data issues (e.g., wrong filtering, join duplication, or bot traffic). How did you detect and fix it?

Round 2: Python & Validation Practice
Data Science Lead · 55 min

Describe your Python approach to building a reproducible analysis workflow from raw data to final metrics. What do you validate along the way?
How do you perform model validation for forecasting or performance models (e.g., holdout strategy, backtesting, error metrics)? What do you look for when results don’t meet expectations?
Tell me about a model where assumptions were questionable. How did you test sensitivity/robustness and decide whether to proceed or revise?
When you work independently on a well-defined analytical problem, how do you decide scope, document assumptions, and ensure your results are reproducible by someone else?

Round 3: Quality, Insights & Communication
Analytics Manager · 55 min

What data quality checks do you consider essential before trusting metrics (e.g., schema drift, distribution shifts, reconciliation across sources)? Provide an example.
How do you detect and respond to metric drift or unexpected outliers in ongoing reporting?
Describe a dashboard/report you built for stakeholders. How did you choose the story, define metric calculations, and validate that the numbers matched the analysis?
Give an example of when you changed your recommendation after reviewing validation results or data quality findings. What was the final decision?

Red flags to watch for

Frequently relies on manual spreadsheet steps without reproducibility or clear documentation for analytical outputs
Cannot explain how experimental results translate into decisions (confuses statistical significance with impact)
Shows weak SQL judgment (common join/filter mistakes, lack of sanity checks) or cannot validate data integrity
Assumes model outputs are correct without discussing validation strategy or failure modes
Difficulty communicating limitations and uncertainty; recommendations are not clearly tied to evidence

How much does it cost to hire a Quantitative Analyst in Latin America?

In 2026, a senior Quantitative Analyst in Latin America costs $5,500 to $6,250 a month all-in, versus $9,625 in the US: up to 43% less. Mid-level starts at $3,625.

Country

Mid-level

Senior

Argentina

$3,625

$5,500

Brazil

$4,000

$6,000

Colombia

$3,625

$5,500

Mexico

$4,125

$6,250

United States

$6,625

$9,625

Monthly cost in USD, all-in: the talent's salary plus Teilur's flat 20% fee, from our 2026 rates. The United States row shows what the same role typically costs there.

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Quantitative Analyst Job Description Template