This position will expand statistical capabilities of a small group of computational/data scientists providing data analysis, modeling and interpretation support to a broad range of programs across the organization. You will be a key contributor of statistical expertise, guidance, training and support to the project teams characterizing complex biological systems by multiple high content analytical technologies at the interface of analytical chemistry and biology (e.g. gene expression, proteomics, flow cytometry, HPLC, mass spectrometry, etc.) under a variety of experimental paradigms. You must possess deep expertise in state-of-art statistical theories and practices and have practical experience in adopting them to achieve actionable insights and conclusions that pave path forward for the projects in a statistically sound way. You will identify gaps in the existing statistical capabilities and take initiative in filling these needs. You will work in close collaboration with bench scientists to increase statistical rigor in experiment design and effectively communicate the results of statistical analyses to project teams and program leadership. This will provide highly impactful opportunities to instill statistical rigor in interpretation of the experimental results and effective translation of data analysis results to facilitate decision making at all stages of our biopharmaceutical R&D pipeline. You will co-author and review technical chapters in technical reports, regulatory communications, manuscripts and publications that will be conducted with commitment to the best practices in reproducible research, integrity of the analysis results and documentation of the work. You will communicate with external collaborators, staying up to date with new developments in the field.
Experience in identifying and applying diverse data analysis techniques, such as (non)linear regression, statistical inference, multivariate methods, (un)supervised methods, Monte-Carlo simulation, resampling, machine learning, etc. as required by the project goals
Excellent oral and written communication skills, ability to effectively explain key aspects of complex statistical approaches to cross-functional audience
Solid problem solving and analytical abilities; strong interpersonal skills and fluency in cross-disciplinary communications
Ability to handle multiple projects with demanding timelines and shifting priorities
Fluency with R is highly desired, but can be substituted with previous experience in statistical programming framework (e.g. S+/Matlab/SAS) combined with ability and motivation to learn new programming environment
Experience in software programming in one or several languages (e.g. C++/Java/Perl/Python) is a plus
Familiarity with power analysis, DOE, QbD and SPC is a plus
Experience with biopharma R&D is a plus
BS in statistics/applied math/biostatistics or other relevant quantitative discipline and 5+ years of experience, MS and 3+ years of experience, PhD with some postdoctoral experience preferred
Solid understanding of modern applied statistics and experience in using statistical methodology and analysis tools to solve variety of complex problems
Demonstrated ability to independently conduct complex statistical analyses of large heterogeneous datasets
Level of experience (years):
Mid Career (2+ years of experience)
How to apply:
Please mention NLP People as a source when applying
About Momenta Pharmaceuticals
Momenta is a biotechnology company with a product pipeline of both complex mixture generic and novel drugs.
Our complex mixture generics and follow-on biologics effort is focused on building a thorough understanding of the structure-process-activity of complex mixture drugs to develop generic versions of marketed products. While we use a similar analytical and development approach across all of our product candidates, we tailor that approach for each specific product candidate.
Our first objective is to apply our core analytical technology to thoroughly characterize the structure of the marketed product. By defining the chemical composition of multiple batches of a marketed product, we are able to develop an equivalence window which captures the inherent variability of the innovator's manufacturing process. Using this information, we then build an extensive understanding of the structure-process relationship to design and control our manufacturing process to manufacture reproducibly an equivalent vers