For consideration of any open positions, please send your resume to:
HRDEPT@i-mabbiopharma.com

Description:

I-Mab (Nasdaq: IMAB) is an innovation-driven global biopharma company focused on the discovery, development and commercialization of novel and highly differentiated biologics for immuno-oncology and autoimmune diseases. The Company's mission is to bring transformational medicines to patients around the world through innovation. I-Mab's globally competitive pipeline of more than 15 clinical and pre-clinical stage drug candidates is driven by its internal discovery and global partnerships for in-licensing, based on the Company's Fast-to-Proof-of-Concept and Fast-to-Market development strategies. The Company is progressing from a clinical stage biotech company into a fully integrated global biopharmaceutical company with cutting-edge R&D capabilities, a world-class GMP manufacturing facility and commercial capability. I-Mab has offices in Beijing, Shanghai, Hangzhou and Hong Kong in China, and Maryland and San Diego in the United States. For more information, please visit http://ir.i-mabbiopharma.com and follow I-Mab on LinkedIn, Twitter and WeChat.

The Translational Medicine team at I-MAB Biopharma US Limited located in San Diego, California is seeking a highly motivated Translational Data Science Lead (TDSL), at Principal Scientist/Senior Scientist level, to develop and execute the translational data science strategy for portfolio assets. Using data driven approach, the successful candidate will be responsible for generating novel and proprietary insights for portfolio assets and actionable hypotheses for indication selection, patient stratification, and combination strategy. Working closely with the Clinical Biomarker team, the TDSL will lead the analysis of clinical biomarker data to enable Go/No-Go decision and support the programs as they advance through development. The position will report to the head of Translational Medicine team in US.

The successful candidate must have a good understanding of immune-oncology, and with a strong background in computational biology, genetics, and data science. The candidate will be effective in the use of relevant computational tools and demonstrate advanced data analysis techniques to high-dimensional biological data. Further, the successful candidate will have a strong oral and written communication skills, be able to effectively collaborate and partner with multiple internal and external teams.

Location: San Diego, California

Responsibilities:

• Define and apply computational biology, genetics, and data analytics approaches to enhance disease and pathway understanding for portfolio assets, and to generate actionable hypotheses for indication selection, patient stratification, and combination strategy
• Identify and manage leading service providers in computational biology and data sciences to execute translational data science strategy
• In partnership with Clinical Biomarker team, lead the analysis of clinical biomarker data to enable Go/No-Go decision, to elucidate the mechanism of action of therapeutic assets, and to identify biomarkers in predicting clinical response
• Drive technical excellence and robustness in data analysis and maintain a state-of-the-art knowledge in computational biology and data sciences

Qualifications:

• PhD degree in Systems Biology, Bioinformatics, Computational Biology, Immunology, Cancer Biology or related field, with 2-5+ years post-graduate industry experience
• Extensive experience gaining biological insights in analyzing high-dimensional biological data
• Experiencing using coding languages such as Python or R, to analyze biological data
• Expertise in methods that enable functional interpretation of biological data including co-expression, causal inference, pathway enrichment, etc.
• Expertise in genetic/genomic integration (e.g. with scRNAseq, proteomics) is preferred
• Industry and/or translational research experience is preferred
• Experience navigating a matrixed organization is preferred
• Experience in wet lab techniques and the generation of biological data is a plus