Are you looking to be involved in applying your skills in bioinformatics to real clinical data sets? Do you have a passion to work with large data sets using your computational tools in a highly collaborative environment? Eli Lilly is looking to grow our bioinformatics team supporting Immunology. You will operate in a dynamic and cross functional environment, working closely with discovery scientists, clinical researchers, IT, and statisticians to enable research related to drug discovery and biomarker discovery and development with a specific focus on autoimmune diseases. All informatics work will be done in a “reproducible research” environment. You will share standard methodologies with others with the opportunity to adopt new methods. This entails using molecular profiles and multiple internal and external biomedical databases to help understand disease diversity and then to use it progress drug discovery and helping define relevant patient population. Are you highly familiar with biological research related to one or more disease and related areas in autoimmune disorders? We could use your expertise.
Have you proven ability to develop and use the tools in the “reproducible research” environment using Rmarkdown, R, and dplyr? Using your strong coding skills you will utilize a key requirement in this position as all analysis is done in this environment. You will also exhibit your expertise in collaborative software developments such as Github. Your computational strengths including good bioinformatics programming skills in scripting or programming languages and related bioinformatics packages would be a phenomenal asset to our critical capabilities in analyzing clinical data. Your shown ability to provide testable hypothesis to project teams based on systematic analysis of data from pre-clinical and clinical samples is valuable.
Some highlights of this role include:
Analysis of baricitinib clinical data from a genomic perspective to support internal efforts, additional indications, and publications.
Collaborate to enable various aspects of drug/biomarker discovery or development in support of the autoimmune portfolio deliverables, which range from novel target discovery to Phase 3.
Propose, conduct analysis, and provide interpretation supporting key project aims. Present results and provide scientific reports as needed or requested by the project teams as a component of project reviews, landmarks or governance in a manner fit for cross-functional team consumption.
Use internal and external data sources such as but not limited to genome variation, genotype-phenotype association, molecularly characterized disease state databases, etc., to routinely deliver assimilated information and insights relating to target of interest or pathway of interest, appropriateness of pre-clinical models, mechanistic understanding in perturbation studies, biomarkers supporting pharmacodynamics or efficacy and hypothesis relating to patient stratification.
Participate in the evaluation of new vendors, services, platforms in genomics arena for eventual application for scientific investigations such as (but not limited to) disease characterization or diagnostic applications.
Apply expertise in IT Informatics and Statistics. Collaborate around data analysis methods and application. Engage IT capabilities and infrastructure team to help meet data storage and data processing needs. Work with the application development group to help develop and implement solutions for data access and visualization for the broader Lilly scientific community.
Represent Immunology, Tailored Therapeutics in external collaborations with both academic centers and commercial companies. Provide analysis or related support as needed in context of such collaborations. Facilitate bringing in data/knowhow or learning into the broader organization.
Contribute to external scientific community through presentations, abstracts, publications and participation in appropriate scientific conferences.
At Lilly, we unite caring with discovery to make life better for people around the world. We are a global healthcare leader headquartered in Indianapolis, Indiana. Our 39,000 employees around the world work to discover and bring life-changing medicines to those who need them, improve the understanding and management of disease, and give back to our communities through philanthropy and volunteerism. We give our best effort to our work, and we put people first. We’re looking for people who are determined to make life better for people around the world.
PhD degree in medicine, biology, computational or statistical sciences or related discipline with relevant experience in one or more aspects of bioinformatics.
Postdoctoral training in related areas with expertise in computational methods, tools and databases utilized in genomic and genetic data analysis preferred.
Lilly is an EEO/Affirmative Action Employer and does not discriminate on the basis of age, race, color, religion, gender, sexual orientation, gender identity, gender expression, national origin, protected veteran status, disability or any other legally protected status.
Hands-on experience of analyzing data generated using many of the following a) Microarrays - Expression / Genotyping, b) Next Generation Sequencing platforms.
Experience in applying computational systems biology or integrative analysis approaches to facilitate interpretation and hypothesis generation from results of genetics, genomics and epigenetics studies. Ability to rapidly prototype novel methods for suitable scientific problems.
Project experience in biomarker discovery, target discovery and validation, mechanisms of action of drugs, correlating genomic/genetics data with clinical and biochemical phenotypes.
Expertise in a range of biological databases and use for translational or basic research. Experience with publicly and commercially available bioinformatics pathway/network analysis tools, genomics and genetics databases. Experience with high performance cluster computing queuing systems such as SGE. Deep expertise in clustering algorithms including NMR, GMM, SOM, and ensemble methods.