Biomarker Statistics & Programming Specialist (Pharma) (s837)

  • Contract Duration: 8-12 Months
  • Research & Development
  • Bridgewater, NJ

Website Lenox Executive Search


6+ month extendable Contract role. High preference for candidate to work onsite at least one day per week in Bridgewater, NJ but also open to 100% remote candidates. Must have M.S in a Statistics, Mathematics, Computer Science or related field and at least 2 years experience in the pharmaceutical/biotech industry.

Lenox Executive Search is seeking an experienced Biomarker Statistics & Programming Specialist to fill an 6+ month extendable contractual role with a global pharmaceutical company in Bridgewater, NJ.. Must have strong knowledge of advanced statistical concepts and techniques in particular for high-dimensional data (cross-validation, multiple testing correction, feature selection).

REQUIREMENTS for the Biomarker Statistics & Programming Specialist:

  • M.S in a Statistics, Mathematics, Computer Science or related field
  • At least 2 years experience in the pharmaceutical/Biotech industry
  • Strong knowledge of advanced statistical concepts and techniques in particular for high-dimensional data (cross-validation, multiple testing correction, feature selection)
  • Expertise in omics data bioinformatics(RNAseq and single-cell RNAseq),proteomics and statistical analysis
  • Experience in Machine Learning algorithms: penalized regression, SVM, random forest
  • Strong knowledge of R
  • Python would be a plus
  • Demonstrated strong project management, interpersonal and communication skills (be able to understand discussions in a cross-functional setting in english)

RESPONSIBILITIES of the Biomarker Statistics & Programming Specialist:

  • Perform tasks under the guidance of statistical lead on statistical analyses, modeling, reporting, analytic interpretations, and slide development analysis for biomarker data.
  • Develop R modules and pipelines to standardize disease identification.
  • Assist project lead in study design, power calculation, analysis and reporting for genomic datasets such as RNASeq, gene expression, Olink data (proteomics), FACS datasets.
  • Apply relevant regression and machine learning tools on biomarker related questions such as patient stratification, diagnostic biomarkers, responder vs Non-responder status etc.
  • Research and implement new ways to show data in graphs, dashboard and Shiny apps.
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