Statistician

Environment and background

Our focus at the Center for Applied Molecular Medicine (CAMM) is improving cancer outcomes by discovering optimal approaches to personalized therapy. Our vision is that a patient’s own metrics will determine their most effective treatment. They might include molecular tumor markers, the dynamics of intracellular processes, or the behavior of cells across diverse microenvironments. We investigate a broad range of biological systems, including cell culture, mouse models, and patient-derived tissues.

Position responsibilities

We are seeking a talented statistician with a strong interest cancer biology and expertise in the statistical analysis of biological data.  The candidate will interact frequently with biological scientists and clinicians to understand their experiments, data, and project goals. The ability to communicate effectively with biologists and clinicians on their own terms is essential. The successful applicant will be capable of designing experiments, providing compelling data visualizations, and applying appropriate statistical analyses of experimental data.  Data sets may include high-dimensional multi-omic data, time series from live cell microscopy, and complex clinical data from electronic medical records. The candidate must be willing to perform low-level data manipulations, including locating/downloading data from public resources, populating databases, and data reformatting.

Key success factors in the performance of this position include a high level of attentiveness to detail, the ability to collaborate closely with others from diverse disciplines, flexibility in technologies, and a willingness to learn.

Requirements

1. Masters or PhD in statistics, biostatistics, quantitative sciences (physics, or applied mathematics) or engineering (electrical engineering, computer science, industrial engineering).
2. Three or more years’ experience in hands-on statistical data analysis in the life sciences.
3. Proficiency in:
   1. Design of Experiments
   2. Power calculations
   3. Parametric and non-parametric tests
   4. Linear, general linear, and mixed-effects models
   5. Outlier detection
   6. Statistical theory
4. Extensive hands-on expertise with the R data analysis platform, including the ggplot2 package
5. Ability to effectively present statistical concepts and research results to a scientific audience
6. Effective technical writing skills