Staff Scientist

ARKANSAS CHILDREN'S IS A TOBACCO FREE WORKPLACE. FLU VACCINES ARE REQUIRED. ARKANSAS CHILDREN'S IS AN EQUAL OPPORTUNITY EMPLOYER. ALL QUALIFIED APPLICANTS WILL RECEIVE CONSIDERATION FOR EMPLOYMENT WITHOUT REGARD TO RACE, COLOR, RELIGION, SEX, SEXUAL ORIENTATION, GENDER IDENTITY OR EXPRESSION, NATIONAL ORIGIN, AGE, DISABILITY, PROTECTED VETERAN STATUS OR ANY OTHER CHARACTERISTIC PROTECTED BY FEDERAL, STATE, OR LOCAL LAWS.

This position has been designated as safety sensitive and cannot be filled by a candidate who is a current user of medical marijuana.

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Work Shift:

Please see job description for details.

Time Type:

Full time

Department:

CC033020 Clinical Research Operations

Summary:

We are seeking a highly motivated Staff Scientist with computational expertise in bioinformatics and/or computational biology to join our multidisciplinary research team studying the microbiome and host-microbe interactions of relevance to infections occurring during childhood. The successful candidate will lead computational analyses focused on the microbiome and other high-throughput sequencing datasets (e.g., RNASeq) to support translational research focused on respiratory and other infections in children. This individual will work closely with Dr. Matthew Kelly (Section Chief, Infectious Diseases, Arkansas Children’s Hospital), a faculty member at the Arkansas Children’s Research Institute. 



This role offers the opportunity to engage in innovative research at the intersection of genomics, microbial ecology, and child health, while working closely with clinical investigators, laboratory scientists, students, and external collaborators. The ideal candidate will hold a doctoral degree in a relevant field and demonstrate strong expertise in bioinformatics and/or computational biology. Proficiency in scripting languages such as Python or Perl, along with experience using shell environments (e.g., Bash) and statistical programming in R, is required. Entirely remote work arrangements (including for applicants residing outside of Arkansas) will be considered for qualified applicants, with regular communication, mentorship, and collaboration via teleconferencing. Periodic travel for scientific meetings or team retreats may be encouraged but is not required.



Current projects in our research group include:

BRAVE Kids Study: NIH R01-funded project focused on SARS-CoV-2 infections in children

Host-microbe interactions in a novel SARS-CoV-2 human challenge model: NIH R21 and American Lung Association-funded project that is performing a multi-omic analysis using previously collected samples

Microbial ecology in the human upper respiratory tract: NIH R21-funded project that is studying microbial interactions of relevance to colonization and infection by bacterial respiratory pathogens

Evaluation of a nasal probiotic candidate in an infant non-human primate model: pilot study being conducted in collaboration with the California National Primate Research Center

Whole-genome sequencing for surveillance of hospital-acquired infections

Other studies in development are focused on routes of community transmission of RSV and use of deep metagenomic sequencing to evaluate for Streptococcus pneumoniae carriage in children



KEY RESPONSIBILITIES



Research Data Analyses (90%)

Perform comprehensive analyses of microbiome sequencing data (e.g., 16S rRNA gene, metagenomic, and metatranscriptomic datasets), with a focus on microbial ecology, taxonomic and functional profiling, and host-microbe interactions. 

Apply and develop computational workflows for multi-omic data integration (e.g., microbiome + host transcriptome). 

Collaborate closely with other members of the research team to design and complete analyses, and prepare summaries of results for publications, conference presentations, or grant applications.

Maintain and optimize reproducible pipelines using tools such as DADA2, Kraken2, HUMAnN, and relevant statistical frameworks (e.g., R, Python). 

Participate in regular project meetings and provide progress updates. 

Support long-term data infrast