Biostatistical Scientist（远程职位）

这条招聘信息来自 Macks Lifesciences，岗位是 Biostatistical Scientist。职位属于「IT 与编程」类别，完全远程。公司不限制候选人的居住地，在任何地方都可以申请。

招聘信息单独点名了 Python，也就是说，正是这一项工具的使用经验会成为决定因素。

公司公布了薪酬：$50k - $75k/年。对工作时间，招聘信息没有提出任何条件。

这个职位通过了自动核查：凡是要求外国工作许可、签证担保、特定国籍，或者必须居住在指定国家的招聘信息，都不会进入列表。

Macks Life Science is a computational life-science company working at the intersection of bioinformatics, scientific computing, and applied research. We partner with researchers, healthcare organizations, and academic collaborators to turn complex biological and clinical data into rigorous, actionable insight. Alongside our core service work, we operate an R&D division building toward long-term, high-impact projects in the space. We're looking for a Biostatistical Scientist to help us bring statistical rigor to research design, data analysis, and evidence generation across our projects, from clinical and genomic datasets to applied pharmacogenomics research. The Role As a Biostatistical Scientist, you will design and execute statistical analyses that support research integrity across our projects and collaborations. You'll work closely with researchers, data scientists, and domain experts to ensure every study, from a small pilot dataset to a larger applied research initiative, is built on sound statistical methodology. Responsibilities

* Design statistical analysis plans for research studies, including sample size and power calculations

* Perform statistical analyses on clinical, genomic, and experimental datasets using appropriate methods (regression, survival analysis, Bayesian methods, multivariate analysis, etc.)

* Collaborate with researchers and mentors to ensure studies are designed with statistical rigor from the outset

* Interpret and communicate statistical findings clearly to both technical and non-technical stakeholders

* Review and validate statistical methodology in research submitted for publication

* Ensure compliance with relevant statistical and regulatory reporting standards (e.g., CONSORT, STROBE) where applicable

* Collaborate cross-functionally with bioinformaticians, data engineers, and research fellows on ongoing projects

* Stay current with emerging statistical methods and their application to genomics, pharmacogenomics, and clinical research

* Contribute to internal training and mentorship for early-career researchers and fellows on statistical best practices

Requirements

* Master's or PhD in Biostatistics, Statistics, Epidemiology, or a closely related quantitative field

* Demonstrated experience applying statistical methods to biological, clinical, or genomic data

* Proficiency in R, Python, or SAS for statistical computing

* Strong understanding of experimental design, hypothesis testing, and common biases in biomedical research

* Experience with statistical software and reproducible analysis workflows (R Markdown, Jupyter, version control)

* Excellent written and verbal communication skills, with the ability to explain statistical concepts to non-statisticians

* Meticulous attention to detail and strong documentation practices

Nice-to-Haves

* Experience in pharmacogenomics, clinical trial biostatistics, or genomic epidemiology

* Familiarity with regulatory statistical guidelines (FDA, EMA, ICH)

* Prior experience mentoring or reviewing the statistical work of junior researchers

* Publication record involving statistical methodology or applied biostatistics

What We Offer

* The opportunity to shape statistical rigor and research methodology at an early-stage, computational life-science company from the ground up

* Direct collaboration with our founding team and research fellows across multiple ongoing projects

* Flexible, remote working structure

* [Insert compensation structure once defined, e.g., base pay, contract terms, or equity discussion]

Macks Life Science is an equal opportunity employer. We do not discriminate on the basis of race, religion, color, national origin, gender, sexual orientation, age, marital status, veteran status, or disability status. Originally posted on Himalayas