Biostatistician 2

The Center for Population Health Sciences (PHS) at Stanford University is seeking a Biostatistician 2 to work with investigators from across the University on data analysis projects, with a focus on bringing demonstrated expertise in electronic health record data and administrative claims data to these collaborations. The Stanford Center for Population Health Sciences (PHS) is dedicated to improving population health by harnessing the power of interdisciplinary research and data analytics. PHS promotes collaboration among researchers, clinicians, and community partners to facilitate innovative research and practical solutions aimed at enhancing health at a population level. The center employs various methodologies, including health informatics, predictive analytics, and community-engaged research, to address pressing public health issues like chronic diseases and mental health. In addition to research, PHS is committed to education, providing training and mentorship for emerging public health and medical professionals to equip them with the necessary skills to tackle contemporary health challenges. Through its comprehensive approach, the center seeks to inform health policies and clinical practices, striving to influence both local and global health initiatives while fostering a culture of health-conscious innovation and collaboration within the community.

This position requires working with some independence, consulting with investigators to refine research questions, define hypotheses, and to design studies and devise analysis plans. The candidate will also work with senior statistician(s) to implement analysis plans and publish findings and present results orally to clinical investigators. Strengths include experience in interdisciplinary collaboration, statistics mentoring, and self-motivated problem solving. The ideal candidate will have interests in causal inference, ethical implementation of machine learning, and experience in working with Cosmos, MarketScan, and CMS claims data.

Duties include:
•    Application of causal inference methods such as entropy balancing, instrumental variables, regression decomposition, and propensity scoring approaches
•    Work with electronic health record datasets such as Cosmos and Truveta to conduct large real-world observational data analysis
•    Applying epidemiologic concepts for appropriate study design principles
•    Communicating clearly and effectively across clinical and policy disciplines
•    Using clinical terminology and disease etiologies when working with clinical experts and communicating within that context
•    Taking complex statistical and analytical approaches and tailoring them for the audience with a teaching orientation
•    Mentoring and teaching students, junior analysts, and trainees in medicine, economics, and public health studies
•    Constructing complex data pipelines with extract, transform, and load processes (ETL) for large datasets
•    Using private and public administrative claims datasets such as MarketScan, Medicare, and/or Medicaid.
•    Writing methodology and results sections in manuscripts for medical and public health journals
•    Working with the OMOP common data model (either as a developer or a user)
•    Validating raw data and assessing its usefulness for downstream processing
•    Flexibly managing several collaborations and working as a collaborator on multiple projects
•    Knowledge of, or ability to learn several commonly applied statistical methods such as hierarchical regression models with random intercepts and slopes, parametric survival models including accelerated failure time models, effect modification and moderation methods, understanding of the basic principles of risk adjustment within the CMS framework, econometric modeling for cost variables with highly skewed distributions, generalized linear models including repeated measures analysis, machine learning approaches such as XGBoost, Explainable Boosting Machines (EBMs), random forests, random survival forests, cluster analysis, latent class analysis, and other classification approaches

* - Other duties may also be assigned

The expected pay range for this position is $115,103 to $134,261 per annum. 

Stanford University provides pay ranges representing its good faith estimate of the salary or hourly wage the university reasonably expects to pay for a position upon hire. The pay offered to a selected candidate will be determined based on factors such as (but not limited to) the scope and responsibilities of the position, the qualifications of the selected candidate, departmental budget availability, internal equity, geographic location and external market pay for comparable jobs. At Stanford University, base pay represents only one aspect of the comprehensive rewards package. 

The Cardinal at Work website (https://cardinalatwork.stanford.edu/benefits-rewards) provides detailed information on Stanford’s extensive range of benefits and rewards offered to employees. Specifics about the rewards package for this position may be discussed during the hiring process.

Consistent with its obligations under the law, the University will provide reasonable accommodations to applicants and employees with disabilities. Applicants requiring a reasonable accommodation for any part of the application or hiring process should contact Stanford University Human Resources at stanfordelr@stanford.edu. For all other inquiries, please submit a contact form.

Stanford is an equal employment opportunity and affirmative action employer. All qualified applicants will receive consideration for employment without regard to race, color, religion, sex, sexual orientation, gender identity, national origin, disability, protected veteran status, or any other characteristic protected by law.