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Statistician
Free template · 2026

Statistician Job Description Template

A ready-to-use Statistician job description for 2026, with the interview scorecard and questions to hire one. Copy it as is or customize it free in about 15 seconds.

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Includes the job description, an interview scorecard and questions for every round.

Mid-level in Latin America
$3,625/mo
Same role in the US
$6,625/mo
You save
Up to 45%

All-in monthly cost, 2026 rates.

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Statistician

We’re hiring a mid-level Statistician to drive reliable analysis from raw data to actionable conclusions. You will design and execute statistical methods, validate assumptions, and communicate results clearly to product and engineering partners.

Responsibilities

  • Develop and apply statistical models and methods to solve business and product problems
  • Design analysis plans, including study design, sampling considerations, and hypothesis testing strategies
  • Clean, transform, and QA datasets to ensure statistical validity and reproducibility
  • Evaluate model performance and uncertainty, and select appropriate metrics for decision-making
  • Run and interpret experiments and/or observational analyses, including diagnosing confounding and bias risks
  • Translate findings into clear narratives for non-technical stakeholders, including recommended next steps
  • Collaborate with cross-functional partners to define success criteria and align on measurement approaches

Must-have qualifications

  • Hands-on experience applying statistical inference (hypothesis testing, confidence intervals, uncertainty quantification)
  • Practical experience building and validating regression and/or machine learning statistical models (with diagnostics)
  • Strong understanding of data quality, bias, and assumptions in analytical work
  • Experience designing analyses with clear documentation and reproducibility practices
  • Ability to communicate statistical results and tradeoffs to non-technical stakeholders
  • Comfort with SQL and/or statistical programming to query, manipulate, and analyze data

Nice to have

  • Experience with A/B testing or experimentation analysis and causal inference concepts
  • Knowledge of time series analysis and monitoring/forecasting techniques
  • Experience with interpreting model drift or data distribution shifts
  • Familiarity with statistical tooling/workflows such as notebooks, version control, and automated reporting

What success looks like in 12 months

  • Measurement and analysis plans are created and followed
  • Model quality and validity improve over time
  • Experiment/analysis results are trustworthy and actionable
  • Reproducible analytical workflows are standard
  • Stakeholder alignment and clarity

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]

Want it tailored to your stack, seniority and time zone?

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Interview scorecard

Score every candidate against the same bar

Deliver reliable, decision-ready statistical analysis by building correct models, validating assumptions, and communicating clear recommendations.

Outcomes to measure

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

  • Measurement and analysis plans are created and followed. Measure: Produce documented analysis plans for [N] initiatives with stakeholder sign-off and no critical assumption/QA gaps in post-review
  • Model quality and validity improve over time. Measure: Achieve agreed-upon performance targets for [M] models (e.g., predictive metrics and calibration checks) and reduce analysis defects/incidents by [X]%
  • Experiment/analysis results are trustworthy and actionable. Measure: Interpret results with correct uncertainty handling for [N] experiments/analyses, with decision accuracy meeting [X]% against pre-defined criteria (or equivalent internal acceptance metric)
  • Reproducible analytical workflows are standard. Measure: Increase reproducibility coverage to [X]% of deliverables with versioned code, documented assumptions, and repeatable data queries
  • Stakeholder alignment and clarity. Measure: Attain stakeholder satisfaction score of [X]/10 (or equivalent) for clarity of statistical explanations and recommendations across [N] partner reviews

Competencies and weights

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

Statistical inference mastery · 24%
Correct inference, uncertainty, and assumptions are the foundation of trustworthy conclusions. Evaluated in Round 1.

  • Weak: Provides point estimates without appropriate uncertainty or misstates assumptions behind tests/intervals
  • Meets the bar: Chooses appropriate tests/intervals and clearly explains what assumptions must hold
  • Strong: Selects and justifies inference methods end-to-end, including uncertainty sources and robustness checks

Modeling and diagnostics · 20%
The role requires building regression/ML statistical models and validating them with diagnostics and performance checks. Evaluated in Round 2.

  • Weak: Fits models but cannot explain diagnostic outcomes or failure modes
  • Meets the bar: Applies diagnostics (residuals/calibration/overfitting checks) and tunes approaches based on evidence
  • Strong: Designs modeling strategy with rigorous diagnostics, addresses leakage/bias risks, and improves calibration/performance with clear tradeoffs

Data quality and assumptions · 16%
Analytical correctness depends on QA, handling missingness/outliers, and understanding how data issues impact assumptions. Evaluated in Round 2.

  • Weak: Does not verify data integrity and relies on raw data without checks
  • Meets the bar: Performs data QA, documents assumptions, and handles missingness/outliers appropriately
  • Strong: Proactively tests for bias/confounding risks, validates assumptions with evidence, and modifies analysis when violations are detected

Reproducible analysis execution · 14%
Independently delivering well-defined work requires structured documentation and repeatable workflows. Evaluated in Round 1.

  • Weak: Work is difficult to reproduce due to missing documentation or uncontrolled data/code changes
  • Meets the bar: Maintains clear analysis notebooks/scripts, version control practices, and consistent documentation
  • Strong: Builds repeatable pipelines from query to results, including clear assumptions, audit trails, and easy stakeholder review

Stakeholder communication · 14%
You must translate statistical outputs into decisions and recommendations for non-technical partners. Evaluated in Round 3.

  • Weak: Presents results with excessive jargon or fails to connect findings to decisions
  • Meets the bar: Explains results, uncertainty, and limitations in plain language and answers follow-ups directly
  • Strong: Tailors explanations to audience needs, surfaces key tradeoffs, and recommends next actions grounded in statistical reasoning

SQL and statistical programming · 12%
Querying and analyzing data efficiently is required for daily effectiveness in this role. Evaluated in Round 3.

  • Weak: Struggles to write/adjust queries or has difficulty interpreting query/analysis outputs
  • Meets the bar: Writes correct SQL/analysis code for data extraction, transformation, and summary statistics
  • Strong: Produces efficient, well-structured queries/analysis code with tests/guards for data correctness
Interview questions

Questions for every interview round

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

Round 1: Statistics fundamentals & reproducibility
Hiring Manager · 45 min

  1. Tell us about a time you designed a hypothesis test or confidence interval. What assumptions did you check, and how did you handle violations?
  2. Walk through how you would create an analysis plan for a new measurement problem: what sections would you include and how would you document assumptions and QA steps?
  3. Describe a reproducibility issue you encountered in analytics (e.g., mismatched data versions, inconsistent filtering). How did you prevent it going forward?
  4. How do you communicate uncertainty (confidence intervals, p-values, and/or other uncertainty sources) to a stakeholder who needs a decision?

Round 2: Modeling & diagnostic thinking
Senior Statistician · 45 min

  1. Explain how you would build and validate a regression model. What diagnostics would you run, and what outcomes would change your approach?
  2. Describe a time model performance was misleading due to data problems (e.g., leakage, selection bias, missingness). What did you identify and how did you fix it?
  3. How do you evaluate whether modeling assumptions hold (e.g., linearity, independence, distributional assumptions)? Give an example.
  4. When would you prefer an alternative model or method over your first attempt? Describe the decision criteria.

Round 3: Applied communication & decision impact
Cross-functional Partner (Product/Engineering) · 40 min

  1. Share an example where you translated statistical results into a clear recommendation. What did you emphasize, and what did you explicitly avoid overstating?
  2. How do you interpret and present effect size and uncertainty when results are borderline or inconsistent?
  3. Describe a situation where you used SQL/statistical programming to uncover a data quality issue. What was the issue and what changed in the analysis?
  4. What is your approach to aligning with stakeholders on success criteria before analysis begins? How do you confirm you measured the right thing?

Red flags to watch for

  • Relies on point estimates without appropriate uncertainty or clear assumptions
  • Cannot describe diagnostics or explains model validity superficially
  • Frequent issues with irreproducible work (missing documentation, uncontrolled filters/data versions)
  • Communicates results in a way that overstates conclusions beyond statistical support
  • Unable to demonstrate SQL/statistical programming competence for querying and analysis work

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

In 2026, a senior Statistician 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.

Compare costs in the calculator →

Frequently asked questions

What does a Statistician do?

Typical responsibilities include: develop and apply statistical models and methods to solve business and product problems; design analysis plans, including study design, sampling considerations, and hypothesis testing strategies; clean, transform, and QA datasets to ensure statistical validity and reproducibility. The full list is in the template above.

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

In 2026, a senior Statistician 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.

Can I customize this Statistician job description?

Yes. Teilur's free Job Description & Scorecard Generator rewrites it for your stack, seniority, time zone and hiring urgency in about 15 seconds. No sign-up needed.

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