Lilly Director/Senior Director - Computational Biology in Boston, Massachusetts
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 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.
Director/Senior Director - Computational Biology
Akouos is seeking a talented and highly motivated Director/Senior Director of Computational Biology. The candidate will be responsible for leading the computational biology efforts of Akouos and will work in close collaboration with members of the Research team within Akouos as well as throughout other divisions of Eli Lilly. The candidate will be responsible for experimental design, analysis, and interpretation of large data sets including bulk RNA-seq, single-cell/single-nucleus RNA seq, single-cell ATAC seq, single-cell barcoding strategies, spatial transcriptomic data, and imaging analysis. The job requires innovative problem solving in all parts of the process from sequencing, estimating gene and transcript expression, applying statistical methods for dimensionality reduction, differential analyses and clustering, to biological interpretation and multimodal data integration. Reporting to the Chief Scientific Officer, this individual will apply these assays in service of developing novel gene therapies for hearing loss and disorders of the ear.
As the Head of Computational Biology, you will develop and apply cutting edge machine learning and bioinformatic methods to integrate and analyze diverse data in order to enable deep characterization of cellular states in data sets generated from human disease and physiology, in animal models, and in response to candidate therapeutics.
Develop and deploy barcoding strategies to enable the collection of robust and reproducible data from highly multiplexed in-vivo experiments.
Develop and deploy both classical data science and cutting-edge ML methods to analyze data from diverse ‘omic modalities, including transcriptomic and imaging data, addressing challenges such as distribution shift, experimental artifacts, data sparsity, and more
Work with lab colleagues to design experiments that generate datasets that are fit for purpose for machine learning, including ones generated explicitly for training ML models
Lead yearly and quarterly planning, set impactful goals, generate team budget and forecast, and align with cross-functional stakeholders
Engineer robust, reusable platform components in partnership with the Information Technology team.
Ph.D. in computational biology, genetics, computer science, bioinformatics or a related discipline.
Minimum of 5 years' experience in a relevant field, post-PhD.
Additional Skills & Preferences:
10+ years practical experience using and developing cutting-edge methods for analyzing biological data sets, including extensive experience with genomic and/or transcriptomic data sets with single-cell RNA-seq experience strongly preferred.
5+ years working in industry, including experience with managing projects and deliverables.
Experience working with multiplexed barcoding strategies coupled with viral delivery strongly preferred.
Experience and demonstrated ability to work closely with diverse teams including IT professionals, engineers and scientists,
Demonstrated ability to architect and build reusable code infrastructure and work with engineering teams
Strong fundamentals in applied multivariate statistics Expertise in machine learning algorithms
Strong programming skills in Python and R Ability to communicate effectively and collaborate with people of diverse backgrounds and job functions in a fast-paced environment
Passion for making a difference in the world
Some understanding of virology, human physiology or disease biology
Strong publication record in machine learning, computer vision, or life sciences Familiarity with cloud computing services (e.g., AWS or GCP)
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