Real World Data Scientist at Tempus

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 Data Analytics 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 Real World 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 Data function and collaborate with internal and external stakeholders in the design, analysis, interpretation and publication of clinical real world studies
Contribute to study protocols and statistical analysis plans. The RWD scientist will then perform sample size and power calculations, execute interim and final statistical analyses, communicate the findings to the stakeholders, and prepare written reports
Work on complex problems, exercising judgment in selecting and adapting methods as appropriate
Work in 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
Comply with all applicable regulations and Company procedures

Required Experience:

Ph.D. or Masters Degree in Biostatistics, Statistics, or Epidemiology with a minimum of 2+ years (Ph.D.) / 4+ years (MS/MA) relevant experience working with clinical, epidemiologic or genomic studies
Thorough understanding of clinical trial methodology and statistical methods including methods for time to event analysis
Computational skills using Python, R or SAS, especially relevant statistical tools and packages

Ideal candidates will possess:

Extensive 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
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 biologySelf-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 supporting data science teams in model building and validation
Client facing or consulting experience and comfort with presenting results to stakeholders
Proficient in SQL

 
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