Asst. Scientist in Miami, Florida

Assistant Scientist, SOM (Sr. Biostatistician)

The Assistant Scientist, SOM (Sr. Biostatistician) carries out a particular research agenda in support of the University's research goals and objectives. Moreover, the assistant scientist contributes to the scientific community by publishing research findings and promoting the Sylvester Comprehensive Cancer Center (SCCC) and University of Miami (UM) reputation as a leader in the field. The Assistant Scientist, works closely with the Assistant Director of Biostatistics, Dr.

Fei Ye and other faculty members within the Biostatistics and Bioinformatics Shared Resources (BBSR) of SCCC to provide comprehensive support in data management, methodological development, advanced biostatistical and bioinformatic analyses, clinical trial design, and result interpretation of findings. They play an integral role in collaborative studies and actively contribute to grant proposals and research projects.

Qualifications:

• PhD or equivalent doctoral degree required in Statistics, Biostatistics, or Bioinformatics.
• A minimum of 1 year of full-time experience in biostatistical analysis, bioinformatics, or statistical research methodologies, including expertise in applying advanced statistical techniques to real-world datasets.
• Demonstrated expertise and experience working with clinical trials, observational studies, and animal studies.
• Experience with omics data (such as genomics, proteomics, metabolomics), and bioinformatics tools.
• Proficiency in R programming, with extensive experience in utilizing R for statistical analysis, data processing, and visualization.
• Publication record demonstrating contributions to applied statistics and biomedical research.

Key Responsibilities:

• Support the Biostatistics component of BBSR on statistical aspects of Cancer Center research related to clinical trials, epidemiologic studies, and laboratory-based investigation.
• Assists in designing research projects, including the specification of hypotheses, sample size calculation and power analysis, randomization, and development of statistical analysis plan.
• Performs data management tasks, including dataset creation, cleaning, verification, and database management (e.g., REDCap).
• Conducts complex statistical analyses on observational studies and clinical trials, applying techniques including regression models, multiple imputation, non-parametric methods, Bayesian framework, and predictive model development and validation.
• Implements bioinformatic pipelines and performs data analysis to support omics studies, including genotype data, bulk and single-cell RNAseq data, high-dimensional phenotype data, pathway and functional analysis, network analysis, polygenic risk score derivation and validation, etc.
• Provides statistical expertise in data visualization, study design, and simulation studies. Develops R or Python scripts for data analysis, statistical modeling, and machine learning techniques, ensuring reproducibility and efficiency.
• Implements and optimizes computing workflows using high-performance computing clusters.
• Engages in statistical consultations with collaborators, presenting findings and addressing statistical inquiries.
• Contributes to manuscript and grant preparation and revisions by drafting statistical methods and results sections, verifying numerical results, and creating tables and figures.
• Effectively communicate with supervisor and collaborators; present analysis results to collaborators during meetings; actively contribute to the preparation of written reports and oral presentations summarizing data analysis results, grant applications and renewals, and publication of research projects.
• Remain current with new statistical methodology applicable to cancer studies, and incorporate major useful advances in practice.
• Serve as a member of the Cancer Center's Protocol Review Committee and Data Safety and Monitoring Committee.
• Adheres to University and unit-level policies and procedures and safeguards University assets.

This list of key responsibilities is not intended to be all-inclusive and may be expanded to include other duties or responsibilities as necessary.

Knowledge, Skills, and Attitudes:

• Statistical Analysis (Expert):
  Extensive experience with descriptive statistics, univariate testing, regression models (linear, logistic, Poisson, negative binomial, lognormal, Cox, mixed-effects, GEE, penalized), propensity score matching, meta-analysis, and machine learning methods.
• Bioinformatic Analysis (Advanced):
  Experienced in gene differential expression analysis, bulk and single-cell RNAseq data analysis, GWAS, PheWAS, and MeWAS (preferred but not required).
• Data Management (Expert):
  Proficient in handling large-scale, high-dimensional datasets, including omics and electronic health records (EHR). Skilled in data acquisition, integration, and quality assurance for large datasets.
• R Programming (Expert):
  Extensive R coding experience with proficiency in RStudio, RMarkdown, and common Bioconductor packages.
• Computing…