Doctorate in Biostatistics, Data Science, or relevant field with 3-4 years of experience, Master's with 6-7 years, or Bachelor's with 7-8 years of relevant experience.
Strong analytical thinker with excellent communication skills and experience with real-world data.
Proficient R programmer, including tidyverse, and experienced with Git in collaborative environments.
Solid skills in generating Tables, Listings, and Graphs using R packages.
Familiarity with ICH guidelines and FDA/EMA regulatory standards in programming.
Extensive experience working with large healthcare datasets, including claims and electronic health records.
Leadership experience in team settings, either in management or project lead roles.
Knowledge of CDISC conventions and some experience with SDTM and ADaM models.
Responsibilities
Provide tailored real-world data solutions for life sciences research.
Translate analytic specifications into R code for dataset creation and statistical analysis.
Act as a subject matter expert on various data types including EHR and clinico-genomics data.
Apply knowledge of cancer biology and epidemiology to develop analytic code.
Mentor junior statistical programming staff and foster their development.
Collaborate with cross-functional teams to deliver client research studies effectively.
Contribute to the enhancement of analytical tools and processes within the organization.
Ensure output quality by providing expert feedback on statistical analysis plans.
Benefits
Opportunities for professional development and mentorship.
Access to cutting-edge research and innovative data solutions.
Collaborative work environment with cross-functional teams.
Impactful role contributing to advancements in life sciences.
Work on meaningful projects that influence patient outcomes.
Full Job Description
Responsibilities:
Team provides customized real-world data and real-world evidence solutions to address the most important research questions across clinical development, market access, and commercial use cases for our life sciences partners
Independently translate analytic specifications from a statistical analysis plan into R code to create analytic datasets, generating descriptive and inferential statistics, data visualizations, often involving Client variables or complex statistical methods, in consultation with the study principal investigator
Serve as subject matter expert on appropriate use cases for, and nuances of, the variety of different Flatiron data modalities, including EHR-derived real-world data, clinico-genomics data, ML-extracted data, and claims data
Develop a proficient understanding of cancer biology, therapy, and/or epidemiology across multiple major tumor types and appropriately apply this understanding when crafting analytic code
Provide mentorship and support to more junior statistical programming staff
Collaborate with cross-functional stakeholders across our medical and scientific organization to execute and deliver on client-sponsored research studies in an accurate, effective, and timely manner
Contribute to continuous improvement of Flatiron's proprietary analytical tooling and templates, at times acting as liaison to the relevant teams and stakeholders
Continue to develop a deeper understanding of real-world data and related methodologies used to generate real-world evidence
Work closely with Epidemiology and Biostatistics to assure output quality by providing expert feedback on SAP, Analytic/TLF specifications from functional perspectivly
Requirements:
Doctorate degree (e.g., PhD, ScD, DrPH) in Biostatistics/Statistics, Data Science, Bioinformatics, Biological Sciences, Public Health, Math, or a closely related field with 3-4 years of relevant experience, or a Master's degree with 6-7 years of relevant experience or a Bachelor's degree with 7-8 years of relevant experience
In addition, you're an analytical thinker and excellent communicator with experience analyzing real-world data (e.g., healthcare claims or electronic health records)
Excellent programmer in R (including tidyverse) and are proficient working in Git-based environments (e.g., Github, Gitlab)
Solid experience in creating Tables, Listings, and Graphs using R packages
Create/review programming documents (e.g., programming plan, specification for datasets and output template)
Knowledge of ICH guidelines, FDA / EMA / other regulatory authority guidance from a programming standpoint
Extensive experience with large healthcare-related datasets (e.g., administrative claims, electronic medical records, genomics)
Experience leading teams, either as a manager or project/team lead
Familiar with CDISC conventions, i.e. SDTM and ADaM models (using SAS) and related controlled terminologies, and knowledge or some experience using these models
