Scientist II, Computational Biology Pharma R&D

Passionate about precision medicine and advancing the healthcare industry?

Recent advancements in underlying technology have finally made it possible for AI to impact clinical care in a meaningful way. Tempus' proprietary platform connects an entire ecosystem of real-world evidence to deliver real-time, actionable insights to physicians, providing critical information about the right treatments for the right patients, at the right time.

The Scientist II, Computational Biology Pharma R&D will execute analytical projects and capability builds to advance the Tempus drug R&D platform. This role involves performing complex computational analyses and developing algorithms for advancing cancer precision medicine for patients across the Tempus network.  The ideal candidate will possess strong genomic analytical skills, experience in applying machine learning and statistical models to big data, and the ability to communicate complex findings to various stakeholders.

Description

• Innovation: Drive continual improvement of the Tempus platform by integrating client feedback, staying ahead of research and industry trends, and championing new opportunities.
• Collaboration:  Work with Research, Engineering & Data Science teams across Tempus’ expansive data science community to develop and deliver innovative computational solutions.
• Drug R&D: Partner with big pharma clients.  Become proficient in the clients’ strategies, drug modalities and pipeline to identify where the Tempus platform can add value.  Co-architect solutions with client science/clinical teams, and design, develop and execute computational research leveraging the Tempus platform to advance their drug R&D programs.
• Independent Contribution: Independently execute complex translational research projects integrating molecular and clinical data from Tempus’ multimodal data platform to extract insights and drive new research opportunities, including new target discovery.
• Develop Expertise:  Become an expert in Tempus’ epidemiological, clinical, ‘omic and imaging data, and the latest tools and techniques to interrogate these. 
• Continuous Improvement: Stay current with industry trends, best practices, and advancements in computational biology for drug R&D.  
• Scientific Communication: Expert in navigating client interactions; Present highly technical results and methods clearly and meaningfully to diverse sets of external stakeholders

Qualifications

• Education and experience: 
• Either
• PhD and additional 2+ years of working experience 
• Masters and additional 4+ years of working experience
• Combining:
• Quantitative and computational skills (e.g. Computational Biology, Biostatistics/Statistical Genetics, Machine Learning, or Bioinformatics).
• Biological or medical knowledge (e.g. Oncology, Immunology, or Human Disease).
• Genomics and transcriptomics.
• Target, drug or diagnostic discovery, or clinical development.
• Technical/Scientific Skills: 
• Proficient in R, Python, and SQL, and respective packages for computational biology. 
• Strong understanding of cancer biology.
• Applicable knowledge of machine learning and statistical modeling.
• Expertise in one of the following: in vitro data analysis and phenomics, network and systems biology, mechanistic modeling and simulation, knowledge analytics, deconvolution and causal inference, integrative analysis of multi-modal data, real-world evidence, and survival analysis.
• Communication Skills: Excellent written and verbal communication skills, with the ability to present complex information clearly and persuasively to diverse audiences. Comfort in a client-facing role
• Motivated: Thrive in a fast-paced environment and willing to shift priorities seamlessly.

Preferred Skillsets/Background

• Strong peer-reviewed publication record.
• Strong understanding of molecular data and artificial intelligence in drug discovery with experience in integrative modeling of multi-modal clinical and omics data.
• Previous experience working with large transcriptome and NGS data sets.
• Thrive in a fast-paced environment and willing to shift priorities seamlessly.
• Experience with R package development.
• Goal orientation, self-motivation, and drive to make a positive impact in healthcare.