Associate Director/Principal Scientist, Machine Learning/A.I.
Waltham, MA Computational Biology
Job Type

Do you have the passion to join a group whose mission is to pioneer the discovery of a new class of medicines to treat diseases unaddressed by today’s therapies? Does it excite you to be a part of a company with a focus on cancer and genetically-validated targets in other disease areas with unmet medical need?  If yes, you may be a great fit to join Arrakis on our path to bring powerful new therapies to millions of patients.

Your Impact at Arrakis

The mission of Arrakis Therapeutics is to extend small molecule medicines into new realms of biology by discovering and developing compounds that selectively target RNA (“rSM”). By targeting distinct RNA structure/function relationships, Arrakis is generating drug candidates with novel mechanisms of action for molecular targets that are challenging to drug in disease areas with high unmet medical need. Identifying functional elements of the transcriptome that can be targeted by rSM’s and establishing mechanism of action requires integrating data across multiple sources. 

This role will build and lead the machine learning/A.I. group at Arrakis with the goal of extracting and integrating insights from various types of internal chemical and biological data sets. Leveraging your deep knowledge of state-of-the-art data science and machine learning tools, and expertise with large datasets, you will drive innovative science to deliver novel therapeutics to patients. The ideal candidate will enjoy working in a collaborative, fast-paced start-up environment, leveraging a high degree of scientific rigor, creativity and innovation. 

Job Responsibilities

  • Provide both scientific and strategic leadership within the data science organization 
  • Build and directly manage a team of scientists focused on applying advance machine learning tools.
  • Lead projects that generate novel machine learning models and algorithms leveraging our internal datasets to advance and accelerate therapeutic programs
  • Develop robust code in a collaborative code-base to facilitate the development, training, and deployment of models.
  • Effectively translate the results of technical or statistical analyses into actionable predictions and recommendations
  • Work effectively in a cross-functional capacity with the platform biology, target biology, and medicinal chemistry teams to addressing business and scientific needs through-out the company
  • Effectively communicate scientific proposals, analysis outcomes, and project goals and progress across the organization

Interested in learning more about Arrakis? Click on the links below!

Dark Matter Blog: Arrakis achieves escape velocity 

Dark Matter Blog: Following our pole star

Arrakis/Roche Alliance Partnership

Arrakis is an Equal Opportunity/Affirmative Action Employer. All qualified applicants will receive equal consideration for employment without regard to race, color, religion, national origin or ancestry, sex, pregnancy, sexual orientation, gender identity, age, disability, military service, veteran status, genetic information, or any other characteristic protected by law.

  • Ph.D. in Computational Science, Cheminformatics, Computational Biology, or related field
  • 7+ years of relevant industry experience required
  • Direct management experience and project leadership experience is preferred
  • Extensive expertise in common programing and scripting languages (python, R,  C++, etc.) 
  • Demonstrated developing, debugging, evaluating, and applying machine learning algorithms to solve scientific problems with a focus on large chemical and biological datasets
  • Experience writing, testing, and maintaining version-controlled software
  • Excellent written and verbal communication skills with a demonstrated ability to communicate the big picture concepts
  • Exhibits passion and excitement over work and brings a can-do attitude without losing objectivity
  • Adjusts quickly to changing priorities and conditions and copes effectively with complexity and change