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 independent and motivated Translational Bioinformatics Scientist to join our Research group.
What You'll Do:
You will work on an interdisciplinary team to develop new computational and statistical approaches to support precision medicine applications in diabetes and related endocrine and metabolic disorders. The successful candidate will work in an interdisciplinary team, carry out data analysis, apply and develop best-in class algorithms that directly address important biological and clinical questions, and provide strategy and input on new products and services.
Advanced degree (Masters or PhD) in bioinformatics, statistics, biostatistics, epidemiology, oncology, genomics, human genetics, computer science, mathematics or a related field, or 5+ years experience working with genomic and clinical data in diabetes
Experience working with electronic health information and related software systems
Experience in genetic analysis of complex disease and familiarity with common bioinformatics software and file formats
Experience with statistical modeling, data mining and/or machine learning
Experience with Python, R, or other modern programming language
Excellent communications skills
A collaborative mindset
Experience with polygenic risk scores and population genetics
Experience with clinical risk modeling and implementation
Experience with version control (Git) and collaborative software development and testing
Experience with AWS technical stack (EC2, S3, Redshift, etc.)
Experience with Real World Evidence (RWE) and Real World Data (RWD) topics and techniques
Experience with relational databases
Record of meaningful scientific publications
Clinical familiarity with diabetes or cardiometabolic disease and associated pathophysiology, or willingness to learn