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Post-Doctoral Research Scientist-Statistical Genetics - FDE

Indianapolis, Indiana

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Req ID R-2586 Title Post-Doctoral Research Scientist-Statistical Genetics - FDE City Indianapolis State / Province Indiana Country United States

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.

At Lilly, we have a history of addressing the needs of individuals living with Diabetes by providing breakthrough therapies that result in meaningful improvements in patients’ lives. We continue to build on this history through research and development efforts that aim at next generation therapies. We have one of the largest pipelines in Diabetes in the industry and are committed to continuing to build our leadership in Diabetes and its complications through innovation in drug discovery and development. Lilly strives to employ innovative post-doctoral fellows/researchers who are dedicated to the science as well as our community and the world in which we live.

The Diabetes and Complications Therapeutic Area (DCTA) is seeking a post-doctoral fellow who can apply statistical genetics knowledge and methodologies for better interpretation of human genetic data. You should be keen to apply state-of-the-art analytical methods to explore how genetics play in shared connections across type 2 diabetes (T2D) and its related complications. The goal is to develop a refined genetic understanding for disease etiology/mechanism and maximize the value of human genetics in diabetes drug target discovery and validation. One core area of research will be systematically explore and perform in-silico characterization of non-coding trait-associated variants by identifying relevant cell-types (through tissue-specific enhancer enrichment), relevant transcription factors (by enriched motifs in multiple GWAS loci), and target genes that are linked to causal enhancers and mechanisms. The candidate will utilize datasets that are publicly available (GWAS meta-analysis datasets for T2D and range of metabolic traits and other relevant outcomes) along with Lilly-internal clinical trial genetics, and other proprietary deep-phenotyped datasets.

The successful candidate will be part of the Bioinformatics & Genetics group at DCTA and will work in highly collaborative, multidisciplinary environment of scientific excellence. You will have regular interactions with Lilly internal scientists who have equipped labs, infrastructure and deep expertise. Your innovative ideas and contributions will help advance Lilly diabetes drug discovery. Consider joining Lilly and our diverse scientific community!

Key Responsibilities include:

  • Contribute to the developing science of diabetes drug target identification and genetic validation
  • Systematically apply statistical methods that integrates genetic association strength with functional annotations and explore underlying biology/potential causal T2D mechanism(s).
  • Analyze public and Lilly internal datasets to explore how T2D and related complications share common genetic risk profiles
  • Identify and explore how genetic factors contributes to disease susceptibility through relevant endophenotypes, and discover dysregulated pathways for diabetes and related traits
  • Test causal relationships between biological processes (genetics and biomarkers) and clinical outcomes using mendelian randomization framework
  • Responsible for genetic analyses, interpretation and communication of large-scale human genetic and phenotypic data
  • Ability to work with others in a collegiate and collaborative environment
  • Present data at scientific conferences and publish in peer-reviewed journals

Basic Qualifications:

  • PhD degree in statistical genetics, human/population genetics, genetic epidemiology, biostatistics or other related analytical fields

Additional Preferred Skills

  • Proficiency in at least one programming language (ideally Python or R), experience in statistical methods and data modeling in high-dimensional human genetics/genomics environment.
  • Demonstrated experience and knowledge of conducting analyses like large-scale GWAS/meta-analyses, phenome-wide (PheWAS), whole-exome sequencing, mendelian randomization, genomic colocalization etc.
  • Knowledge of modern human large-scale genetics and genomics resources and databases
  • An attention to detail and a drive for rigour
  • Strong verbal and written communication skills with strong peer-reviewed publication record
  • Working experience in diabetes and metabolic diseases is not necessary, but a big plus

This position is not permanent. It is for a fixed duration of two years with potential to extend annually for up to 4 years.

Eli Lilly and Company, Lilly USA, LLC and our wholly owned subsidiaries (collectively “Lilly”) are committed to help individuals with disabilities to participate in the workforce and ensure equal opportunity to compete for jobs. If you require an accommodation to submit a resume for positions at Lilly, please email Lilly Human Resources ( ) for further assistance. Please note This email address is intended for use only to request an accommodation as part of the application process. Any other correspondence will not receive a response.

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.

To submit resume, visit and apply to Req ID R-2586.

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.

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