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Biometrician

Science AI-Augmented

Uses statistics and math to analyze biological data (often for medical or security fields).

B
🔵 Stable
Biometrician has a positive outlook with moderate hiring activity. A reliable path with room to grow.
69
/ 100
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Work Profile

Stress
5/10
Work-Life
8/10
Future Outlook
7/10
Hours / Week
40

Salary Growth

Starting
$75,000
Year 5
$105,000
Year 10
$135,000
Top Earners
$195,000
Promotion Path: Junior Analyst -> Biometrician -> Senior Biometrician -> Director.

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Core Skills and Tools

These are the practical tools, systems, math, and AI skills used in Biometrician roles.

Core Skills

3D modeling
Agricultural sampling
Applied science publication record

Industry Knowledge

Fisheries management
Music industry operations

Math & Analysis

Bayesian hierarchical modeling
Bayesian statistical modeling
Data analysis
Data science modeling
Data visualization
Forestry biometrics
Multivariate statistics
Natural resources biometrics
Spatial and time series analysis
Statistical programming

Requirements

Bachelor's degree requirement
Degree field requirement
Master's degree requirement
PhD / Doctoral degree requirement
Professional certification or license

Systems & Software

ArcGIS Pro and ArcGIS Online
Data visualization development
JMP statistical software
Oracle
Python
R and SAS statistical computing
SQL
Scientific programming languages
ShinyApps development
Word

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Other Potential Career Pivot Paths

Many professionals start in Biometrician roles and later move into higher-paying or more specialized careers by developing additional skills.

Data Scientist

Move into an adjacent analytics role applying modeling and experimentation across business or science domains.

Typical Salary: $110k-$160k

Skills Needed: statistics, programming, modeling, data analysis

Difficulty: 🟡 Moderate

View Career →

How to Get Hired

This section focuses on practical readiness: what skills matter, what tools or certifications may help, and what you can do now to become more employable.

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See the exact skills, certifications, projects, and step-by-step plan to break into Biometrician.

  • Real projects to build experience
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AI Impact & Task Breakdown

AI-Augmented

AI is a core part of modern biometrics (facial recognition, etc.).

How to Stay Valuable

  • Develop judgment, communication, and problem-solving skills
  • Learn how AI tools can support this career instead of ignoring them
  • Move toward strategy, interpretation, and higher-value work over time

AI Scores

Automation Risk: 5.2/10 Human Value: 8.1/10 AI Leverage: 8.0/10

Based on 10 analyzed tasks

More likely automated

  • Build reproducible analysis code
  • Analyze experimental study data

Still requires human judgment

  • Explain findings to research teams
  • Design measurement protocols

How AI Affects This Job

This career includes a mix of tasks. Some are becoming more automated, while others are becoming more valuable because they require human expertise.

Design measurement protocols

Define sampling plans measurement methods and statistical endpoints for biological or agricultural studies

Moderate Automation High Human Value High AI Leverage
Automation: 4/10 • AI Leverage: 8/10 • Human Value: 9/10

Likely adoption: 3-5 years

Design Statistical Plans

Develop sampling and analysis approaches for biological or agricultural studies

Moderate Automation High Human Value High AI Leverage
Automation: 5/10 • AI Leverage: 8/10 • Human Value: 8/10

Likely adoption: 3-5 years

Analyze experimental study data

Apply mixed models variance analysis and other statistical methods to interpret controlled experiment results

Moderate Automation High Human Value High AI Leverage
Automation: 6/10 • AI Leverage: 9/10 • Human Value: 8/10

Likely adoption: 0-2 years

Analyze Experimental Data

Apply statistical models to interpret outcomes and variability in research datasets

Moderate Automation High Human Value High AI Leverage
Automation: 6/10 • AI Leverage: 8/10 • Human Value: 8/10

Likely adoption: 0-2 years

Build reproducible analysis code

Write and maintain scripts for data cleaning model execution and repeatable reporting workflows

High Automation Risk High AI Leverage
Automation: 7/10 • AI Leverage: 9/10 • Human Value: 7/10

Likely adoption: 0-2 years

Collaborate on Study Design

Advise scientists on power endpoints and measurement strategies before research begins

Moderate Automation High Human Value High AI Leverage
Automation: 4/10 • AI Leverage: 7/10 • Human Value: 8/10

Likely adoption: 3-5 years

Assess data quality in field studies

Review collection inconsistencies missing observations and protocol deviations before final analysis

Moderate Automation High Human Value High AI Leverage
Automation: 5/10 • AI Leverage: 8/10 • Human Value: 8/10

Likely adoption: 3-5 years

Prepare Statistical Reports

Document methods assumptions and conclusions for stakeholders or regulators

Moderate Automation High AI Leverage
Automation: 6/10 • AI Leverage: 8/10 • Human Value: 7/10

Likely adoption: 0-2 years

Explain findings to research teams

Translate statistical outputs into practical implications for scientists project leads and sponsors

Low Automation Risk High Human Value High AI Leverage
Automation: 3/10 • AI Leverage: 7/10 • Human Value: 10/10

Likely adoption: 5-10 years

Validate Analytical Workflows

Check code models and outputs for consistency reproducibility and accuracy

Moderate Automation High Human Value High AI Leverage
Automation: 6/10 • AI Leverage: 8/10 • Human Value: 8/10

Likely adoption: 0-2 years

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