Data Scientist​/Statistician; Level 2 - Aerospace Medical Research; AMR

Data Scientist/Statistician (Level
2) - Aerospace Medical Research (AMR
10)

Pro Sidian Seeks a Data Scientist/Statistician (Level
2) - Aerospace Medical Research (AMR
10) headquartered near CONUS
- Oklahoma City, OK to support requirements for Aerospace and Defense Sector Clients.

This CONUS
- Oklahoma City, OK | Data Scientist/Statistician (Level
2) - Aerospace Medical Research (AMR
10) Contract Contingent position currently best aligns with the Data Scientist/Statistician (Level
2) Labor Category. Ideal candidates exhibit the ability to visualize, analyze, and convert data and experiences to meet performance challenges while confidently engaging in productive "Jugaad" and dialogue targeting mission success. Pro Sidian Team Members work to provide Gov't.

- Federal (USA) Sector related Human Capital Solutions for Aerospace Medical Research and Technical Support Services on behalf of The Civil Aerospace Medical Institute (CAMI).

Data Scientist/Statistician (Level
2) - Aerospace Medical Research (AMR
10) Candidates shall work to support requirements for (Aerospace Medical Research Services) and shall work as part of a team in support of Aerospace Medical Research efforts. The candidate will complete tasks and activities contributing to deliverables and core mission functions in the Aerospace Medical Research space.

Must perform work as required, with typical tasks such as:

• Perform analysis of genetic datasets for the Functional Genomics team. This includes command-line operations in a Linux or Unix operating system (e.g., Red Hat Linux or Scientific Linux).
• Run processes in parallel across multiple computer nodes on multi-core computer systems when appropriate for analyses.
• Identify appropriate computational and statistical analyses required to complete
  -bioinformatics objectives for processing data, including processing genetics data (e.g., sequence files) and running statistical analyses on phenotypic datasets.
• Use packages and software such as R/CRAN, Python, and AI/machine learning approaches.
• Review software documentation and perform proper implementation of software.
• Ensure appropriate use of software and parameters to meet requirements of the data, including not violating assumptions of statistical analyses.
• Use model selection and analytical approaches defensible in peer-reviewed literature. Typical analyses of datasets may include basic processing and QA/QC of sequence or microarray files, mapping genetic sequences to a reference genome/transcriptome and performing differential expression analysis; microarray analysis; analyzing genetic variants or SNPs; analyzing chromatin accessibility; analyzing microbiome datasets; performing functional enrichment analyses; and constructing network/pathway diagrams.
• Write scripts and code as necessary to enable high-throughput analyses, including efficient and high-throughput mapping and analysis of next-generation sequence datasets, with sufficient annotation to allow future use of scripts by other team members.
• Provide all scripts, codes, and results to the team.
• Document analytical methods used, including descriptions of names and versions of software along with features used.
• Input information about methods in laboratory notebooks and the electronic journal system of the team.
• Write up methods used in paragraph format at a quality and detail level appropriate for inclusion in peer-reviewed publications.
• Aid other members of the team with compiling scripts or drafting code.
• Install and maintain the computer operating system, packages, patches, and updates as appropriate.
• Keep the data, files, and code backed up.
• Obtain and maintain Collaborative Institutional Training Initiative (CITI Program) certification in human subjects research including Biomedical (Biomed) coursework, and related certification when necessitated by individual projects.

B.S. in Computer Science, Genetics, or a related field

4 years of relevant experience after college graduation

Experience working with a High Performance Computer and running processes in parallel; understand statistics, including descriptive statistics (e.g., means, averages) and more advanced statistics (e.g., generalized linear models, model selection approaches).

Sufficient knowledge of bioinformatics to select and appropriately use methods for genetics statistical/bioinformatics analyses (including assumptions that must be tested, considerations such as normalization, and understanding of when to transform data).

Experience with writing scripts and running commands in a Linux or Unix operating system; advanced experience with R/CRAN for statistical analysis, and advanced experience with Python; advanced experience with bioinformatics analyses of next-generation sequencing data.

Adequate verbal/written communication skills to explain their computations to members of the team, and to write paragraph-style descriptions of their methods sufficient for inclusion in peer-reviewed publications.

U.S. Citizenship Required
- You must be a United…