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.
We are seeking an experienced scientist to join the Computational Biology group at Tempus in a Senior Scientist II - Associate Principal Scientist position (depending on experience). This role involves designing and implementing clinicogenomic studies for research and molecular model development. We are seeking a candidate with extensive experience in the analysis of high-dimensional genomic data, cancer genomics/immuno-oncology, statistics, and machine learning. The successful candidate will carry out data analysis and apply best-in-class algorithms—or develop new algorithms— that directly address important biological and clinical questions.
Key Responsibilities:
Scientific:
- Design, develop, and execute multi-modal computational biology and/or immunology research projects of high complexity.
- Support exploratory research, development and validation studies for molecular subtype characterization, target discovery, and predictive algorithms.
- Develop and implement scientific strategies and experimental work plans, including identifying new methodologies and approaches for large clinicogenomic databases and real world data.
Collaboration:
- Work closely with other cross-functional teams across the R&D and broader Tempus organization (product engineering, operations, clinical genomics labs, medical, science, data science, etc) to communicate research and integrate work plans and approaches.
- Work with Product and Engineering teams to streamline the workflow of computational analyses.
Continuous Improvement:
- Stay current with industry trends, best practices, and advancements in computational oncology research.
- Apply this knowledge to enhance research methodologies and improve overall research quality on the team.
Qualifications
- Education: PhD degree in a quantitative discipline (e.g. Computational Biology, Cancer Biology, Computational Immunology, Biostatistics/Statistical Genetics, Bioinformatics, or similar). Alternatively, a PhD in Molecular Biology or Immunology combined with a very strong record in computational biology.
- Experience: Minimum 2+ years leveraging genomic and multimodal with machine learning approaches to address questions in complex diseases, especially cancer.
- Technical/Scientific Skills: Proficient in R, Python, and SQL. Strong understanding of Cancer, Genomics, and/or Immunology. Extensive prior experience analyzing genomic data, including whole exome sequencing, whole transcriptome sequencing, and/or TCR/BCR immune repertoire sequencing, single-cell RNA sequencing, spatial transcriptomics.
- Leadership Skills: Demonstrated ability to lead and manage complex projects and collaborate with cross-functional teams. Strong strategic thinking and problem-solving skills.
- Communication Skills: Excellent written and verbal communication skills, with the ability to present complex information clearly and persuasively to diverse audiences.
Preferred Qualifications:
- PhD with 2+ to 6+ years of work experience
- Education and experience must combine:
- Quantitative and computational skills for biological data analysis (e.g. Computational Biology, Biostatistics/Statistical Genetics, Bioinformatics, Biomedical Informatics, or Data Science for Health)
- Biological or medical knowledge (e.g. Oncology, Genetics/Genomics, Molecular Biology, or Immunology)
- Multidisciplinary project team leadership experience
- Ability to deliver actionable insights from NGS, clinical, and/or real-world data sets
- Strong understanding of statistical techniques and artificial intelligence in computational biology with experience in integrative modeling of multi-modal clinical and omics data.
- Previous experience working with large transcriptome and NGS data sets, including TCR-seq
- Experience with in R or Python packages for computational biology: Pandas, NumPy, SciPy, Scikit-learn, Jupyter Notebooks, RStudio, tidyverse, ggplot, Git, matplotlib, seaborn, Plot.ly, Docker, AWS.
- Experience with R package development.
- Thrive in a fast-paced environment and willing to shift priorities seamlessly.
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