생물 통계학자

근무 일정

• Biostatisticians work full-time with regular office hours. Overtime may be needed when there’s a tight deadline to meet. Workplace settings may include research institutions, government agencies, or healthcare companies.

일반적인 의무

• Collaborate with healthcare/life sciences experts to design studies and surveys
• Consult on study design, data management, and results interpretation, as needed
• Establish timelines and objectives for biological research study projects
• Review research protocols and recommend statistical analyses
• Determine the appropriate sample size for clinical studies
• Write research protocols and analysis plans
• Assign tasks to assistants or programmers
• Collect biological data through surveys, experiments, archival data, health records, or other sources. Input data into secure databases
• Use data analysis algorithms and statistical approaches to analyze and interpret data
• Use mathematical models to track changes in biological phenomena
• Monitor clinical trials to ensure data quality and procedural adherence
• Conclude and make predictions from data summaries and analyses
• Apply research results to extend biological theory or suggest new projects
• Prepare statistical data, tables, and graphs for reports to regulatory agencies and other stakeholders
• Prepare articles for publication and presentations at conferences

추가 책임

• Write research proposals and grant applications
• Participate in peer review for scientific journals
• Engage in multidisciplinary research teams and contribute to public health studies
• Stay current with developments through literature, professional interactions, and continuing education
• Teach biostatistics courses or seminars

소프트 스킬

• 분석
• 비판적 사고
• 호기심
• 의사 결정
• 디테일 지향
• 논리적
• 목표
• 관찰력
• 영구
• 문제 해결
• 수완
• 강력한 의사 소통 기술
• 팀워크

기술 능력

• Ability to interpret and present biological data findings. Biological data types include:

1. Sequences (DNA and genomes of various species)
2. Graphs (pathway data, genetic maps, structured taxonomies)
3. High-dimensional data (gene expression experiments)
4. Geometric information (molecular structure data)
5. Scalar and vector fields (chemical concentration, electric charge, current fluxes)
6. Patterns (genes, regulatory sequences, protein structures)
7. Constraints (conservation of mass, energy conservation)
8. Images (electron and optical microscopy, radiographic images)
9. Spatial information (spatial relationships and differences)
10. Models (computational models describing form and parameters)
11. Prose (biological literature and annotations)
12. Declarative knowledge (hypotheses and evidence)

• Advanced statistical methodologies and modeling techniques
• Bioinformatics and genomic data analysis
• 데이터베이스 관리 시스템
• Data collection methods, data analysis techniques, data manipulation and visualization tools
• Machine learning algorithms
• Programming languages (Python, Julia)
• Regulatory requirements and guidelines for clinical trials
• Statistical software (e.g., SAS, SPSS)
• Statistics and math applications in biology and health sciences
• Study design

1. Descriptive studies (case reports, case series)
2. Analytical studies (observational studies, experimental studies)

• Survival analysis, longitudinal data analysis, and multivariate analysis

• Government health departments
• Hospitals and healthcare systems
• Non-governmental public health organizations
• Pharmaceutical and biotechnology companies
• Universities and research institutions

Expectations run high because biostatistics demands meticulous analysis and accurate conveyance of complex research findings—with a lot potentially riding on the results! Even though Biostatisticians might not have direct interaction with patients, their work significantly influences the development of health policies and the improvement of patient outcomes.

Biostatisticians spend a lot of time on their computers, analyzing data and compiling exhaustive reports. Some people may find such tasks tedious, but it’s important to stay focused and maintain a high level of attention to detail. The job also requires a commitment to continuous learning as advanced new statistical tools and methodologies come out. 

The biostatistics field is being transformed by breakthroughs in big data analytics, machine learning, and artificial intelligence. These advancements are boosting the capacity to process and interpret vast amounts of data, enabling faster, more accurate conclusions.

The use of extensive datasets is also powering personalized medicine—customized medical treatments based on each patient’s genetic makeup and health profiles.

Online platforms are providing Biostatisticians with an unprecedented wealth of health-related data, which they can use for research (such as gathering population health insights for epidemiological studies). This, in turn, is enhancing the healthcare sector’s understanding and management of health trends and emergencies on a global scale.

Individuals drawn to this career field often have a strong interest in STEM subjects, especially in applying mathematical concepts to solve real-world problems. They may have enjoyed working with computers, engaging in detailed analysis, or participating in science fairs and math clubs.