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(Senior) Principal Scientist, Computational / Systems Biology

About Us

Deep Genomics is at the forefront of using artificial intelligence to transform drug discovery. Our proprietary AI platform decodes the complexity of genome biology to identify novel drug targets, mechanisms, and therapeutics inaccessible through traditional methods. With expertise spanning machine learning, bioinformatics, data science, engineering, and drug development, our multidisciplinary team in Toronto and Cambridge, MA is revolutionizing how new medicines are created.


Where You Fit In

In this role, you will be responsible for leveraging large-scale genetic data-sets to find potential targets and molecular mechanisms for oligonucleotide therapeutics. You will lead the development and implementation of analysis methods, automated data processing  and visualization pipelines for large-scale -omics data. You will organize the activities of bioinformatic scientists and data scientists, and collaborate with other teams, including statistical genetics, ML science, engineering, and biology teams. 


Key Requirements
  • PhD in Computer Science, Bioinformatics, Genomics or related fields with at least 5 years of work experience, preferably in industry setting
  • Extensive experience with -omic data processing and developing standardized processing and analysis pipelines e.g., transcriptomic, proteomics datasets
  • Experience with single-cell analyses, including single-cell RNA-seq data processing and analysis. Experience with additional technologies such as single-cell ATAC is nice to have
  • Experience in visualization of complex genomic data, and automated report generation
  • Ability to perform differential expression, gene co-expression or network analyses
  • Strong scientific programming skills (Python preferred) and experience with high-throughput or cloud compute (especially GCP)
  • Basic understanding of human genetics, such as genomic regions and regulation (e.g., exons, introns, enhancers, promoters, transcription, translation, micrRNAs, RNA-binding proteins etc.); basic understanding of human biology and disease
  • Previous work experience in cardiometabolic or CNS diseases is desirable
  • Familiarity with machine learning or AI models in the context of -omics is an advantage but not required
  • Previous experience in managing direct reports and/or external vendors is desirable
  • Self-driven with critical thinking, intellectual curiosity and commitment to innovation 
  • Excellent communication and interpersonal skills 


What we offer
  • A collaborative and innovative environment at the frontier of computational biology, machine learning, and drug discovery. 
  • Highly competitive compensation, including meaningful stock ownership.
  • Comprehensive benefits - including health, vision, and dental coverage for employees and families, employee and family assistance program. 
  • Flexible work environment - including flexible hours, extended long weekends, holiday shutdown, unlimited personal days.
  • Maternity and parental leave top-up coverage, as well as new parent paid time off. 
  • Focus on learning and growth for all employees - learning and development budget & lunch and learns.
  • Facilities located in the heart of Toronto - the epicenter of machine learning and AI research and development, and in Kendall Square, Cambridge, Mass. - a global center of biotechnology and life sciences.


Deep Genomics welcomes and encourages applications from people with disabilities. Accommodations are available on request for candidates taking part in all aspects of the selection process.

Deep Genomics thanks all applicants, however only those selected for an interview will be contacted.  

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Average salary estimate

$140000 / YEARLY (est.)
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$120000K
$160000K

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What You Should Know About (Senior) Principal Scientist, Computational / Systems Biology, Deep Genomics

Join Deep Genomics as a (Senior) Principal Scientist in Computational / Systems Biology and be part of a trailblazing team transforming drug discovery with artificial intelligence. Based in the innovative environment of Cambridge, MA, you will be at the nexus of bioinformatics and genomics, utilizing large-scale genetic datasets to uncover promising targets and molecular mechanisms for oligonucleotide therapeutics. You'll lead the development of analysis methods and create automated data processing pipelines that simplify the handling of complex -omics data. As a pivotal player, you'll coordinate efforts between bioinformatic, data scientists, and other critical teams, ensuring a cohesive approach towards pioneering new therapeutics. With a PhD in Computer Science, Bioinformatics, or a related field and over five years of industry experience, you’ll apply your extensive experience in -omics data processing to drive significant advancements in drug development. If you are adept in scientific programming, particularly Python, and possess a deep understanding of human genetics and biology, you might just be the perfect fit for our collaborative and dynamic workplace. Plus, we offer flexible work arrangements, competitive compensation, and focus on professional growth, making Deep Genomics not only a great place to work but a responsive community ready to support your career journey. Come and help us reshape medicine for the better!

Frequently Asked Questions (FAQs) for (Senior) Principal Scientist, Computational / Systems Biology Role at Deep Genomics
What are the main responsibilities of the (Senior) Principal Scientist in Computational / Systems Biology at Deep Genomics?

The (Senior) Principal Scientist at Deep Genomics is primarily responsible for leveraging large-scale genetic datasets to identify novel drug targets and molecular mechanisms pertinent to oligonucleotide therapeutics. This role encompasses the development and implementation of sophisticated analysis methods, as well as the establishment of automated data processing and visualization pipelines that handle complex -omics data efficiently.

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What qualifications are required for the (Senior) Principal Scientist role at Deep Genomics?

Candidates interested in the (Senior) Principal Scientist position at Deep Genomics must possess a PhD in Computer Science, Bioinformatics, Genomics, or a related field, along with a minimum of five years of relevant industry experience. Extensive experience with -omic data processing, particularly transcriptomics and proteomics, is essential, along with a solid understanding of human genetics and biology.

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What experience is preferred for the (Senior) Principal Scientist role at Deep Genomics?

While a strong foundation in bioinformatics and systems biology is necessary, applicants for the (Senior) Principal Scientist position at Deep Genomics are preferred to have experience with single-cell analyses, including the processing of single-cell RNA-seq data. Familiarity with machine learning or AI models as they relate to -omics data can be advantageous for potential candidates.

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What technologies should the (Senior) Principal Scientist be familiar with in their role at Deep Genomics?

The ideal (Senior) Principal Scientist at Deep Genomics should be well-versed in scientific programming, particularly using Python, and have a background in high-throughput or cloud computing, especially Google Cloud Platform (GCP). Knowledge in visualization techniques for complex genomic data and experience with differential expression and network analyses is also highly valuable.

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How does Deep Genomics support its (Senior) Principal Scientist in their professional growth?

Deep Genomics encourages continuous learning and development for its employees, including those in the (Senior) Principal Scientist role. The company offers a dedicated learning budget, lunch and learns, and a collaborative environment designed to foster innovation and professional development, ensuring that team members stay at the forefront of computational biology and drug discovery.

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Common Interview Questions for (Senior) Principal Scientist, Computational / Systems Biology
Can you explain your experience with -omic data processing?

In your response, detail your hands-on experience with various -omics data types, emphasizing any pipelines you’ve developed or optimized. Discuss specific projects where your analyses significantly impacted findings, showcasing your technical skills and the rationale behind your methodologies.

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What programming languages are you proficient in, and how do you apply them in your research?

Highlight your proficiency in programming languages like Python and how you use them for data analysis, automation, and visualization. Provide examples of scripts or tools you've created that improved efficiency or accuracy in data processing and the results they yielded.

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How do you approach developing and implementing automated data processing pipelines?

Discuss your systematic approach to developing pipelines, including planning, selecting appropriate tools, coding, and testing. Highlight any frameworks you've utilized and the importance of automation in your workflow, especially in handling large datasets.

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Describe a challenging bioinformatics problem you've faced and how you solved it.

Choose a specific challenge that showcases your problem-solving skills. Explain the context, your thought process, the steps you took, and how this experience contributed to your growth in the field, demonstrating both technical competence and creativity.

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What strategies do you use for collaboration across interdisciplinary teams?

Reflect on past experiences in collaborative projects, emphasizing active communication, understanding team dynamics, and flexibility in working styles. Mention specific tools or methods that facilitate smooth workflows between bioinformatics, ML science, and engineering teams.

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How do you stay updated with the latest advancements in computational biology?

Talk about your ongoing commitment to professional development through attending seminars, participating in workshops, or reading journals. Mention any relevant courses or certifications you’ve pursued to enhance your knowledge in genomic technologies and computational methods.

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Can you give an example of how you've utilized machine learning in your research?

If applicable, detail a project where you implemented machine learning techniques. Explain the problem statement, the algorithms you selected, and the outcomes achieved. This demonstrates your ability to integrate modern technology into bioinformatics.

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What do you consider essential when visualizing complex genomic data?

Discuss the importance of clarity, accuracy, and interactivity when visualizing data. Share any tools or techniques you often apply, explaining how they enhance data presentation and lead to actionable insights for stakeholders.

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How do you ensure the quality of your results in your analyses?

Highlight specific quality control measures you employ, such as validation techniques, reproducibility checks, and peer reviews. Emphasizing your commitment to quality will reassure interviewers of your attention to detail in research.

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What interests you most about working in drug discovery at Deep Genomics?

Share your passion for how AI and computational biology can revolutionize drug development. Discuss any specific aspects of Deep Genomics' mission or projects that resonate with you and align with your career goals, demonstrating your genuine interest in the role.

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Full-time, on-site
DATE POSTED
March 18, 2025

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