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 in the path of 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 Analyst with extensive experience and interest in oncology and/or pharmacology. Top candidates will also have experience working with clinical and research data pipelines, working on cross-functional teams, and implementing machine learning and/or statistical models.

What you will do:

Become an expert on Tempus data generation and aggregation

Use the multimodal Tempus data set to generate data-driven drive change across the organization

Partner with internal and external data users to remove barriers between data and patient impact

Build, deploy, and monitor production-grade data transformation pipelines

Help scale our analytics 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

Qualifications:

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

Experience in data transformation pipelines that directly feed end users actionable data

3+ years professional experience in a direct analytics capacity

Nice to haves:

2+ years in medical data analytics

"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)

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