Senior Scientific Programmer Analyst, IS&T Scientific Computing
SENIOR SCIENTIFIC PROGRAMMER ANALYST, IS&T Scientific Computing
Tracking Code 5322/L2117 Job Description
Lead the technical aspects related to consulting, training, and scientific application package support for Research Computing Services on the Boston University Medical Campus (BUMC). Technical aspects include engagement of research projects, project guidance, personal instruction, tutorials, workshops, workflow/pipeline development, application porting, and code optimization of for research projects. Serve as the primary support contact for BUMC faculty, staff and graduate students on complex projects and issues that require in-depth knowledge of the multiple technical areas within the Research Computing Services Group as well as the core computational research tools used in biostatistics, bioinformatics, microbiology and biochemistry. Work directly with faculty and other academic stakeholders to assess needs and recommend solutions as a representative of the Research Computing team. Act as primary liaison between faculty, staff, and students on BUMC and staff at Research Computing Services.
- MA/MS (PhD preferred) in Statistics, Biostatistics, Bioinformatics, Bioengineering or an equivalent combination of education and experience.
- Strong analytical and personal skills with an ability to manage multiple projects and deliverables, strong attention to detail.
- Extensive experience with life-sciences research, including large scale genetics/genomics research and analysis pipelines.
- Experience with bioinformatics common tools (Samtools, TopHat, Cufflinks etc.).
- Fluency in multiple programming languages particularly R and Python.
- Understanding of basic statistical methods.
- Hands-on experience in Linux scripting and package installation.
- Ability to quickly learn new programming languages and tools.
- Excellent communication skills, and the ability to work well both independently as well as in a team.
- Experience utilizing and scripting for Linux HPC clusters;
- Extensive knowledge of numerical methods, computational methodologies.
- Strong statistics background.
- Understanding and experience in statistical programming environments such as SAS and Stata.
- Good knowledge of clinical study designs, common analysis methods, descriptive and inferential statistics, summarization of data and presentation practices.
- Experience working with sequence, variant and public genomics datasets.