Statistical Geneticist, Diabetes, Obesity, And Complications

Eli Lily

0-3 years

Not Disclosed

Bangalore

10 Jan. 30, 2025

Job Description

Job Type: Full Time Education: B.Sc, M.Sc, B.Pharm, M.Pharm, LifeScience Graduates Skills: Causality Assessment, Clinical SAS Programming, Communication Skills, CPC Certified, GCP guidelines, ICD-10 CM Codes, CPT-Codes, HCPCS Codes, ICD-10 CM, CPT, HCPCS Coding, ICH guidelines, ICSR Case Processing, Interpersonal Skill, Labelling Assessment, MedDRA Coding, Medical Billing, Medical Coding, Medical Terminology, Narrative Writing, Research & Development, Technical Skill, Triage of ICSRs, WHO DD Coding

Job Title: Statistical Geneticist, Diabetes, Obesity, and Complications
Category: Research & Development
Job Type: Full-Time, Regular
Job ID: R-66448

Company Overview:

Eli Lilly and Company is a global healthcare leader based in Indianapolis, Indiana, committed to uniting caring with discovery to improve the lives of people around the world. Our employees across the globe work to develop and bring life-changing medicines to those who need them, improve the understanding and management of diseases, and give back to communities through philanthropy and volunteerism. At Lilly, we strive to put people first and give our best efforts in everything we do.

Position Overview:

The Diabetes, Obesity, and Complications Therapeutic Area (DOCTA) at Lilly focuses on developing new therapeutic approaches for the treatment of diabetes, obesity, and related cardiometabolic diseases. As part of our dynamic and growing team, the Statistical Geneticist will collaborate with cross-functional teams to utilize human genetic data to derive insights and drive data-driven decisions. This role presents a unique opportunity to be at the forefront of scientific exploration in a rapidly evolving research field, working on innovative therapeutics aimed at tackling obesity, diabetes, and cardiometabolic diseases.

Key Responsibilities:

Genetic Data Analysis: Collaborate with software engineers, platform architects, and bioinformaticians to develop and implement auditable pipelines for genetic data analysis, including WGS, WES, and genotyping studies from internal and external sources.
Variant Annotation and Analysis: Develop and use cloud-based pipelines for variant annotation according to ACMG criteria, including population frequency, computational scores, and functional data.
Genetic Analysis Design: Design and implement genetic analyses from multiple data sources, including association studies, rare variant analysis, and polygenic risk score analysis.
Post-computational Analysis: Perform in-depth analysis and interpret findings within biological and clinical contexts, collaborating with computational biologists, translational researchers, and clinical scientists to validate genetic targets.
Scientific Communication: Present and clearly communicate results from genetic analyses through manuscripts, posters, and presentations to both scientific and non-scientific audiences.
Code and Documentation: Engage in code and documentation review within the team and across other DSCB teams, ensuring adherence to industry-standard methodologies for scientific project documentation.

Key Requirements:

Education: PhD or equivalent in Statistical Genetics, Genetic Epidemiology, Population Genetics, or a related field.
Experience: 0-3+ years of post-PhD experience in genetic data analysis.

Additional Skills & Preferences:

Genetic Data Expertise: Proven experience in end-to-end analysis of human genetic data, including experimental design, execution, and biological interpretation.
Genetic Data Formats: Proficiency working with genetic data formats (VCF, BAM/CRAM, BED) and experience with variant annotation pipelines (e.g., SNPeff, ANNOVAR, Varsome).
Programming: Expertise in programming languages such as R or Python for genetic data analysis.
Domain Expertise: Familiarity with metabolism-related fields such as obesity, diabetes, MASH, cardiometabolic diseases, or cardiorenal conditions is strongly preferred.
Cloud-based Environments: Experience with complex analyses in cloud-based environments; prior experience with platforms like DNANexus is a plus.
Additional Data Formats: Experience working with additional data types, including RNA-seq, metabolomic, and proteomic data.
Clinical Data: Experience working with clinical data is a plus.
Multitasking: Ability to manage multiple competing priorities in a fast-paced research environment.
Communication Skills: Ability to communicate complex scientific and computational concepts to both computational and non-scientific audiences.
Team-Oriented: Strong collaboration skills with a customer-focused approach to design thinking.

Why Join Lilly?

At Lilly, we are driven by our mission to create life-changing medicines and improve the health of people around the world. Our collaborative and innovative teams work to make a real impact in addressing the world’s most significant health challenges. As a member of the DOCTA team, you will contribute to groundbreaking scientific research and help develop novel therapies that aim to transform the treatment of diabetes, obesity, and cardiometabolic diseases.

Lilly is an Equal Opportunity Employer (EEO). We are committed to ensuring all individuals have equal opportunities to compete for jobs and support employees with disabilities. If you need accommodations during the application process, please contact Lilly Human Resources at Lilly_Recruiting_Compliance@lists.lilly.com.

#WeAreLilly

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