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 Data Health team focuses on decreasing the time to research for all users of Tempus' data. We build tools, processes, and models to remove or mitigate obstacles to obtaining actionable insights from the Tempus multimodal data set. We use a combination of machine learning, analytics, and knowledge of Tempus operations to reduce internal friction and get people better answers, faster.
We are seeking a highly motivated and capable Senior Data Scientist with experience and interest in oncology and/or pharmacology. Top candidates will also have experience with clinical and research data pipelines, working on cross-functional teams, and implementing robust, needs-driven, machine learning-backed solutions.
What you will do:
- Become an expert on Tempus data generation and aggregation
- Transform the Tempus data set into insightful and appropriate features
- Partner with internal and external data users to remove barriers between data and patient impact
- Build, deploy, and monitor production-grade data transformation and machine learning pipelines
- Scale our analytical and ML capabilities through technical leadership and knowledge sharing
- Combine your knowledge of medical data with Tempus data and tools to increase the value and utility of Tempus data assets
- Technical proficiency in SQL and Python
- Demonstrated ability to communicate technical concepts to audiences with mixed technical backgrounds and experience
- Proven ability to provide data-driven decision support through analytics and visualizations
- Strong background in supervised and unsupervised machine learning
- 3+ years professional experience in a direct data science capacity
- 2+ years in medical data analytics
Nice to haves:
- Big data ETL experience
- Experience in Spark, Hadoop, or other larger-than-memory data manipulation environments
- Familiarity with standard medical ontologies and code systems (RXNORM, ICD9/ICD10/ICD0, SNOMED, etc)
- Sharable code examples demonstrating technical proficiency (Github, Gitlab, etc)