Broad Institute

Description & Requirements

The Vallabh/Minikel lab, located at the Broad Institute of MIT and Harvard, seeks a Computational Biologist II to join the team. The lab is led by Dr. Sonia Vallabh and Dr. Eric Minikel, who became scientists in response to Sonia’s own genetic test report. The goal of our work is to discover, develop, characterize and advance effective treatments for prion disease, a rapidly fatal and currently incurable neurodegenerative disease. You can read more about our mission here: http://www.vallabhminikel.org/

The successful applicant will provide dynamic computational support to multiple diverse, translationally relevant projects in the lab. We aspire for this individual’s work to contribute directly to the development of a therapy for a currently untreatable disease. To seize this opportunity, they must be a self-starter, highly motivated to contribute to the mission of our lab. Being mission driven means that the lab is dynamic, with task lists evolving rapidly depending on what is most helpful at any given time. It also means that we must all bring to work, every day, the attitude that no task is below us. Some of our projects involve "big data." Some involve small data. We work with wide-ranging data types, from genomics, transcriptomics, metabolomics and proteomics to imaging to single-readout experiments in cells and small cohorts of animals and patients. We’re a vertically integrated lab that is constantly forging ahead onto new ground. We’re here to do what’s needed in service of a life-saving drug in our lifetimes. We are primarily a wet lab, and the successful applicant will need to be able and enthusiastic to work closely alongside wet lab biologists, in a spirit of collaboration and without snobbery. They must bring a spirit of critical thinking, be able to troubleshoot independently, and take an active role in seeking advice and guidance as needed.

The Broad is rich with computational resources and expertise, and Dr. Minikel, who trained as a computationalist, will supervise this individual. However, it bears repeating that we are primarily a wet lab - to make the most of this role the applicant will need to be creative, proactive, interpersonally aware, and team-spirited across disciplinary lines.

Qualifications

• PhD in Biology, Computer Science, Bioinformatics, Engineering, Math, Statistics, Physics, or a related quantitative discipline
• Knowledge of biology spanning omics to epidemiology
• Experience with Linux command line environments and strong knowledge of statistics and computational data analysis (R, Python)
• Comfort with multi-contributor collaboration via GitHub
• Robust oral and written communication skills. We all represent the lab and mission.
• Outstanding critical thinking skills
• Willingness and ability to quickly gain new skills and knowledge in relevant domains is a must
• Strong interpersonal and collaboration skills to work in a team-oriented environment
• Ability to skillfully work through differences in perspective
• A self-starting attitude
• Ability to report on progress in a professional manner on a regular basis
• Exquisite attention to detail and organizational capacity.

Responsibilities

The successful candidate will:

• Apply statistical methods to a variety of genomic, transcriptomic, metabolomic, proteomic, and image-based profiling datasets.
• Help to think critically about interpretation, contextualization and visualization of these data.
• Help to maintain organization of the lab’s data across data types.
• Coordinate with sequencing and imaging facilities to receive, organize, conduct quality control, and preprocess of the data.
• Generate data visualizations and assist the team with the effective graphical communication and representation of the findings.
• Provide supervision to a Computational Associate 1.
• Develop new analytical approaches, visualization tools, and automation approaches for routine analysis tasks.
• Advise wet lab scientists on strategies for ensuring the datasets they generate are clean and analysis-ready.
• Listen to problems and challenges from wet lab scientists and advise on experimental design, appropriate statistical tests, QC approaches, dataviz strategies, and automation opportunities.
• Prepare analytical pipelines to be applied on local and cloud-based computational platforms.
• Prepare reproducible git repositories for public release, with commented code, organized analytical datasets, and clear READMEs.
• Prepare summary reports and communicate results to scientists in the lab.
• Join calls with outside collaborators as necessary, prepared to present professionally.
• Contribute to preparation of manuscripts and drafting of analytical methods.
• Flexibly support collaborating labs as needed.

The following examples of public repositories from our lab will help the candidate to get a sense of the computational component of past projects in our lab:

https://github.com/ericminikel/prnp_penetrance https://github.com/ericminikel/prp_lowering https://github.com/ericminikel/nd_trials https://github.com/ericminikel/scaso https://github.com/ericminikel/genetic_support https://github.com/ericminikel/halflife https://github.com/ericminikel/divalent

We encourage candidates to apply as early as possible and will review applications on a rolling basis. Required application documents include a cover letter and CV. Please write a cover letter! Our lab is a unique environment, and we want to get a feel for why you are interested in working here in particular. References will be requested on follow-up. We regret that we will not be able to reply to every applicant. Contact: Sonia Vallabh

We think this is an unusual and exciting opportunity for a Computational Biologist to get on-the-ground experience supporting rare disease drug development, at the fast-paced intersection of academia and industry. If you think so too, we would love to hear from you.