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Statistical Genetics Jobs (NOW HIRING)

Required : • Experience in functional or single cell genomics and/or statistical genetics • Strong experience in modern computational statistics and machine learning • Bachelor's degree and ...

Beef Geneticist

De Forest, WI · On-site +1

$75K - $104K/yr

Evaluate and implement improvements to statistical models, methodologies, and data collection strategies to increase prediction accuracy. * Stay current with advancements in quantitative genetics ...

NY · On-site

$120 - $160/hr

Premier Research is looking for a Statistical Scientist Director to join our Biostatistics team. You will help biotech, medtech, and specialty pharma companies transform life‑changing ideas and ...

Premier Research is looking for a Statistical Scientist Director to join our Biostatistics team. You will help biotech, medtech, and specialty pharma companies transform life-changing ideas and ...

... statistical genetics, and computational biology. • Experience developing production-grade bioinformatics pipelines and software platforms. • Excellent communication and presentation skills.

Showing results 41-60

Statistical Genetics information

What does a statistical geneticist do?

A statistical geneticist analyzes genetic data using statistical methods to identify genetic factors associated with traits or diseases. They often work with large datasets, employ software tools like R or Python, and collaborate with researchers to interpret genetic information for research or clinical purposes.

What are the key skills and qualifications needed to thrive as a statistical geneticist, and why are they important?

To thrive as a Statistical Geneticist, you need a strong background in genetics, statistics, and bioinformatics, often supported by an advanced degree (such as a PhD) in genetics, statistics, or a related field. Expertise with analytical tools like R, Python, PLINK, and genome-wide association study (GWAS) software, as well as familiarity with large-scale genetic datasets, is typically required. Strong problem-solving skills, attention to detail, and effective communication are essential soft skills for interpreting data and collaborating with multidisciplinary teams. These skills are crucial for generating meaningful genetic insights, advancing research, and ensuring accurate analysis in complex genetic studies.

How to become a statistical geneticist?

To become a statistical geneticist, typically a candidate needs a bachelor's degree in genetics, statistics, or a related field, followed by a master's or Ph.D. in statistical genetics, bioinformatics, or computational biology. Developing skills in programming languages like R or Python, understanding genetic data analysis, and gaining experience through research or internships are also important steps.

What are some typical collaborative projects a statistical geneticist might work on within a multidisciplinary research team?

Statistical Geneticists frequently collaborate on projects involving genome-wide association studies (GWAS), analysis of large-scale sequencing data, and development of new statistical methods for genetic data interpretation. These projects often require close teamwork with bioinformaticians, laboratory scientists, clinicians, and data analysts to design studies, interpret findings, and translate genetic discoveries into clinical or biological insights. Such collaborations offer opportunities to contribute specialized statistical expertise while learning from other disciplines, ultimately advancing both scientific understanding and career growth.

What is the difference between Statistical Genetics vs Bioinformatics?

AspectStatistical GeneticsBioinformatics
Required CredentialsDegree in Genetics, Statistics, or related fieldsDegree in Computer Science, Bioinformatics, or related fields
Work EnvironmentResearch labs, academic institutions, healthcare settingsResearch labs, biotech companies, healthcare institutions
Industry UsageGenetic research, disease association studies, population geneticsGenomic data analysis, sequence alignment, data management

Statistical Genetics focuses on analyzing genetic data using statistical methods to understand inheritance and disease associations, while Bioinformatics emphasizes developing computational tools for managing and interpreting biological data. Both roles often collaborate but serve distinct functions within genetic research and healthcare industries.

What is statistical genetics?

Statistical genetics is a field of study that combines statistics and genetics to analyze and interpret genetic data. It focuses on understanding the genetic basis of traits and diseases by applying statistical methods to data from genome-wide association studies, family studies, and population genetics. Statistical geneticists develop models and tools to map genes that contribute to complex traits, estimate heritability, and predict genetic risk. Their work supports advances in personalized medicine, agriculture, and evolutionary biology.
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Infographic showing various Statistical Genetics job openings in the United States as of August 2026, with employment types broken down into 2% As Needed, 76% Full Time, 20% Part Time, 1% Contract, and 1% Nights. Highlights an 95% Physical, 1% Hybrid, and 4% Remote job distribution.

Postdoctoral Fellow-MSH-32910-030

Mount Sinai Hospital

Manhattan, NY • On-site

$53K - $73K/yr

Full-time

Re-posted 18 days ago


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Job description


Postdoctoral Fellow in Psychiatric Genomics (computational)
Details of the research project:
The Mullins Lab at the Icahn School of Medicine at Mount Sinai is seeking aPostdoctoral Fellow with a PhD in Statistical Genetics, Computational Biology or a similar field, to lead statistical genetic studies of suicide phenotypes. The Psychiatric Genomics Consortium Suicide Working Group is the world's largest resource for conducting genetic studies of suicidality and aims to elucidate the genetic etiology of suicide outcomes through large-scale genetic studies of common and rare variants, characterization of the shared and distinct genetic architectures of suicide outcomes, elucidation of genetic heterogeneity within suicide outcomes with respect to sex and ancestry, identification of biologically relevant pathways, tissues and cell-types, and exploration of potential causal relationships with modifiable risk factors. The Postdoctoral Fellow will also work on projects in the Mount Sinai Million Health Discoveries Program involving the analysis of genetic and clinical data.
Roles & Responsibilities:
The Postdoctoral Fellow will work under the supervision of Dr. Niamh Mullins, Associate Professor in the Department of Psychiatry, Artificial Intelligence and Human Health, and Genetics and Genomic Sciences, and the Charles Bronfman Institute for Personalized Medicine. Responsibilities will include statistical analysis of genetic and genomic datasets and electronic health records, manuscript writing, oral presentations at lab meetings, consortium calls, and scientific conferences, and engaging in regular collaborative meetings with teams of scientists locally and internationally.
Responsibilities
Roles & Responsibilities:
The Postdoctoral Fellow will work under the supervision of Dr. Niamh Mullins, Associate Professor in the Department of Psychiatry, Artificial Intelligence and Human Health, and Genetics and Genomic Sciences, and the Charles Bronfman Institute for Personalized Medicine. Responsibilities will include statistical analysis of genetic and genomic datasets and electronic health records, manuscript writing, oral presentations at lab meetings, consortium calls, and scientific conferences, and engaging in regular collaborative meetings with teams of scientists locally and internationally.
Qualifications
Eligibility:
Required:
  • PhD in Statistical Genetics, Biostatistics, Computational Biology, Bioinformatics or similar field
  • Programming skills (particularly R, Unix/ Linux)
  • Experience working with genetic datasets
  • Proficiency in running statistical genetics software
  • Excellent written and oral communication skills in English
  • Ability to work independently and as part of a collaborative international team
  • Working knowledge of GWAS and post-GWAS analyses
  • Experience with diverse ancestry genetic data
  • Familiarity with psychiatric nosology

SPOC-UAW Local 4100 at Icahn School of Medicine (Post Docs), J18 - AI and Human Health - ISM, Icahn School of Medicine
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