Principal Research Associate/Research Scientist, in vivo Pharmacology at Deep Genomics

Cambridge, Massachusetts, United States

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Not SpecifiedCompensation

Senior (5 to 8 years), Expert & Leadership (9+ years)Experience Level

Full TimeJob Type

UnknownVisa

Biotechnology, Drug Discovery, RNA TherapeuticsIndustries

Requirements

Degree in Pharmacology, Biology, Molecular Biology, Genetics, Biochemistry or a related field with a BS and 6-8+ years of relevant experience, a MS and 4-6+ years of relevant experience, or a PhD and 0-2+ years of relevant pharmaceutical or biotechnology experience
Strong understanding of molecular biology and tissue sample processing for RNA and protein readouts as well as a familiarity with Next-Generation Sequencing techniques (NGS)
Experience with rodents, running animal studies, and in vivo study design is highly desirable
Experienced in design, execution and analysis of molecular assays
Excellent communication, organizational skills and a willingness to learn
Ability to work independently and manage priorities in a fast-paced technical environment
Preferred Qualifications
Hands-on experience using liquid handlers is preferred (ex. Beckman Echo, Agilent Bravo and Hamilton STAR/STAR V.)
Past experience with RNA editing and/or RNA oligonucleotides
Knowledge of histology and immunohistochemical assays and quantification is a bonus
Demonstrated experience working in cross-functional teams
Experience working in an advanced data and computing environment

Responsibilities

Perform standard molecular biology techniques in a consistent and reproducible manner (e.g. PCR, qPCR, ddPCR, ELISA, Western blot, Jess/Leo capillary-based Western blot, etc.)
Support the development of novel molecular, cellular and functional assays on animal samples for our internal therapeutic pipeline
Plan and execute in vivo rodent studies to enable drug discovery
Collaborate with a cross-functional team in a dynamic, matrixed environment to achieve shared company deadlines
Clearly document experiments, standard operating procedures (SOPs) and data in an electronic lab notebook (ELN)
Communicate and present experimental design and findings with colleagues and project teams

Skills

in vivo Pharmacology

rodent models

molecular biology

PCR

qPCR

ddPCR

ELISA

Western blot

Jess/Leo

RNA modalities

ADAR

ASO

mRNA

siRNAs

tissue processing

animal models

Deep Genomics

AI-driven drug discovery and development

About Deep Genomics

Deep Genomics focuses on drug development in the biotechnology sector by utilizing artificial intelligence to explore RNA biology and discover potential therapies for genetic conditions. The company's main product, the AI Workbench, employs data-driven predictions to identify new drug targets. This tool has evolved over time, with the latest version, AI Workbench 3.0, set to enhance its capabilities in targeting complex genetic diseases. Deep Genomics serves a diverse clientele, including pharmaceutical companies and research institutions, and generates revenue through the development and licensing of its AI Workbench. The goal of Deep Genomics is to accelerate the drug discovery process and improve treatment options for patients suffering from genetic disorders.

Toronto, CanadaHeadquarters

2014Year Founded

$230.3MTotal Funding

SERIES_CCompany Stage

AI & Machine Learning, BiotechnologyIndustries

51-200Employees

Benefits

Company Equity

Health Insurance

Dental Insurance

Vision Insurance

Life Insurance

Disability Insurance

Professional Development Budget

Risks

Increased competition from companies like Insitro and Recursion Pharmaceuticals.

Rapid technological advancements may render current AI Workbench obsolete.

Ethical concerns and regulatory scrutiny could delay product development timelines.

Differentiation

Deep Genomics uses AI to unravel RNA biology for drug development.

The AI Workbench identifies novel drug targets and therapeutic candidates.

BigRNA model advances RNA disease mechanism discovery and candidate therapeutics.

Upsides

AI integration with CRISPR allows precise gene editing and therapeutic development.

AI-driven platforms optimize clinical trial designs, reducing costs and time to market.

AI identifies novel biomarkers, expanding target discovery capabilities.

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