ABOUT US

Deep Genomics is at the forefront of using artificial intelligence to transform drug discovery. Our proprietary AI platform decodes the complexity of RNA biology to identify novel drug targets, mechanisms, and therapeutics inaccessible through traditional methods. We bring together a multidisciplinary team spanning machine learning, bioinformatics, data science, engineering, and drug development, in Toronto, ON and in Cambridge, MA, to revolutionize modern medicine.

BASIC QUALIFICATIONS

• PhD in Computational Biology, Bioinformatics, Genomics, or a related field
• 5+ years of relevant postdoctoral and/or industry experience in computational biology, bioinformatics, or multi-omics analysis.
• Demonstrated expertise in NGS data analysis, including amplicon sequencing, bulk and single-cell RNA-seq, and other profiling methods.
• Strong programming skills (e.g., R, Python), with experience in cloud computing and data management.
• Experience managing, mentoring, and developing direct reports.
• Proven ability to lead and manage all computational aspects of cross-functional projects/programs and communicating complex computational concepts to both technical and non-technical audiences.
• Strong analytical and problem-solving skills, with a focus on delivering practical, impactful solutions.
• Ability to align scientific/technological innovation with business objectives, driving technological differentiation and competitive advantage.

PREFERRED QUALIFICATIONS

• Familiarity with AI/ML applications in drug discovery is a strong plus.
• Experience in a clinical-stage biotechnology company is highly desirable.
• Track record of scientific achievement through publications, patents, or successful project leadership in computational biology or bioinformatics.

• Team Leadership: Directly supervise, mentor, and develop a team of four computational biologists. Foster a collaborative, innovative, and high-performing team culture that drives the success of the company’s platform. Set clear goals and ensure the timely, high-quality delivery of project milestones.
• Strategic Direction: Define and execute the computational biology strategy in alignment with company objectives. Facilitate effective communication and collaboration between the computational biology team and other functions (e.g., Target Identification, Machine Learning, Engineering).
• Pipeline Oversight: Oversee computational pipelines for discovery-stage and preclinical programs. Build and maintain robust, scalable analysis pipelines and workflows to capture and analyze large-scale experimental datasets. In collaboration with the ML team, produce data packages that demonstrate the value of the AI platform in drug discovery.
• Platform Evaluation: Partner in the regular evaluation of the platform to ensure it can define and handle valuable large datasets and complex analyses according to the product roadmap. Collaborate with cross-functional teams to enhance the platform’s computational infrastructure, integrating advanced technologies to improve capabilities.
• Data Integration & Experimental Design: Identify and integrate external datasets in collaboration with the engineering and machine learning teams. Contribute to the design of experiments for model evaluation in partnership with the Target Identification and experimental teams.