Data Scientist II

Overview

Fred Hutchinson Cancer Center is an independent, nonprofit organization providing adult cancer treatment and groundbreaking research focused on cancer and infectious diseases. Based in Seattle, Fred Hutch is the only National Cancer Institute-designated cancer center in Washington.

 

With a track record of global leadership in bone marrow transplantation, HIV/AIDS prevention, immunotherapy and COVID-19 vaccines, Fred Hutch has earned a reputation as one of the world’s leading cancer, infectious disease and biomedical research centers. Fred Hutch operates eight clinical care sites that provide medical oncology, infusion, radiation, proton therapy and related services, and network affiliations with hospitals in five states. Together, our fully integrated research and clinical care teams seek to discover new cures to the world’s deadliest diseases and make life beyond cancer a reality.

 

At Fred Hutch we value collaboration, compassion, determination, excellence, innovation, integrity and respect. Our mission is directly tied to the humanity, dignity and inherent value of each employee, patient, community member and supporter. Our commitment to learning across our differences and similarities make us stronger. We seek employees who bring different and innovative ways of seeing the world and solving problems.

 

The Data Scientist II provides collaborative and innovative analytic support for the Biostatistics, Bioinformatics, and Epidemiology (BBE) Program in the Vaccine and Infectious Disease Division (VIDD), leveraging novel statistical and mathematical modeling techniques to analyze demographic, behavioral and epidemiological data from clinical and observational studies in order to develop and utilize data-informed epidemic models of infectious diseases with primary focus on estimating the effectiveness of HIV preventive interventions based on expanded use of different formulations of pre-exposure prophylaxis (PrEP). The Data Scientist will be responsible for the design, parameterization and calibration of different classes of dynamic models, development of novel computational approaches, visualization of data, and communication and presentation of results. This position reports to a Principal Staff Scientist in the Biostatistics, Bioinformatics, and Epidemiology program in the Vaccine and Infectious Disease Division.

 

At Fred Hutchinson Cancer Center, all employees are expected to demonstrate a commitment to our values of collaboration, compassion, determination, excellence, innovation, integrity, and respect.

Responsibilities

• Develop and refine mechanistic mathematical models to study the effectiveness and efficiency of individual and combined interventions for HIV prevention.
• Develop a modeling framework to process and analyze epidemiological data from multiple sources (clinical and observational studies, demonstration projects) to parameterize and calibrate complex epidemic models.
• Develop novel statistical and mathematical modeling approaches for integration of complex data sets as model inputs.
• Build open-source, reusable scripts.
• Communicate results from modeling analyses to technical and non-technical audiences.
• Collaborate with and advise team members, including post-docs, statistical research associates, bioinformaticians, staff scientists.
• Lead and contribute to manuscripts and presentations on novel computational approaches for evaluation of HIV preventive programs and interventions.
• Other duties as assigned.

Qualifications

MINIMUM QUALIFICATIONS:

• A Bachelor’s degree or higher in computer science, data science, statistics, informatics, or equivalent.
• Core competency in at least one of the following: genomics, natural language, image processing, medical records or claims.
• Experience in software development in the context of machine learning, such as R/Python, Weka, or others.
• A sound understanding of software development best practices (e.g., version control, unit testing, regression testing).
• A fundamental understanding of machine learning, both supervised and unsupervised, and experience with machine learning tools and applications.
• A strong background in both formal statistics and predictive analytics, and some experience with the analytic process.
• Software development in the context of machine learning, ideally in R or Python.
• Experience with working in a cloud environment (e.g., Amazon’s EC2).
• Experience working with an agile development methodology.
• Knowledge of best practices in data analysis and scientific computing (e.g., literate programming, reproducible research).
• Experience with using and managing data in clinically regulated environments.

PREFERRED QUALIFICATIONS