Associate Principal Scientist

Key Skills

Data Analysis, Data science techniques, Effective communication skills, Machine learning techniques, Python Programming, R Programming, SQL

Job 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.