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 Real World Evidence team at Tempus partners with external Pharma, biotech and academic institutions to provide best in class data, analysis and methodological guidance to Tempus’s real world data offering. We are seeking a highly motivated and capable Data Scientist with extensive experience and interest in design and analysis of pharmacoepidemiological studies to join our team.

Responsibilities:

• Participate in clinical projects with external Pharma, academic and other partners
• Represent the Real World Evidence function and collaborate with internal and external stakeholders in the design, analysis, interpretation and publication of clinical real world studies
• Work on complex problems, exercising judgment in selecting and adapting methods as appropriate
• Work with interdisciplinary groups of scientists, engineers, and product developers to translate research into clinically actionable insights for our clients
• Stay current with the latest methodological advances in real world studies
• Build infrastructure including reusable code
• Comply with all applicable regulations and Company procedures

Required Experience:

• Advanced degree (Masters with 4 years of experience or PhD) or Bachelor’s degree with 6 years of experience in data science, bioinformatics, biostatistics, epidemiology, immunology, public health, or related fields. 
• Computational skills using Python, R or SAS and SQL, especially relevant statistical tools and packages

Ideal candidates will possess:

• Strong data manipulation and analysis skills 
• Ability to tackle large, ambiguous problems 
• Strong communication and presentation skills
• Self-starter
• Familiarity with machine learning techniques and the advantages and disadvantages of different approaches, especially with respect to predictive and prognostic algorithms in medical research
• Experience in cancer genetics, immunology, or molecular biology
• Self-driven and works well in interdisciplinary teams
• Collaborative mindset, an eagerness to learn and a high integrity work ethic
• Sharp attention to detail and passion for delivering high quality and timely analytics deliverables
• Able to effectively present research results to study team and other collaborators, including results interpretation and drawing appropriate inferences based on study design/statistical methods as well as assessment of study limitations

Nice to have:

• Experience with version control and software testing
• Experience with time to event analysis and methodology
• Experience working in oncology Phase II-IV clinical trials and/or experience with the analysis of RWD studies (e.g. using claims, EHR or registry data sources)
• Hands-on experience in helping to prepare regulatory submissions to the FDA
• Experience supporting data science teams in model building and validation
• Client facing or consulting experience and comfort with presenting results to stakeholders

#LI-GL1