E 4) (Temporary)

University of Washington•Seattle, WA

1d•$90,000 - $105,000

About The Position

OVERVIEW: The Computational Ophthalmology Lab is leading collaborative efforts on large multidisciplinary research projects, using Big Data and machine learning. The Research Scientist / Engineer 4 (RS/E 4) will play a critical role in these important studies. This is a tremendous opportunity to grow as part of a very productive team. Under the technical guidance of an experienced scientist, the Research Scientist/Engineer 4 (RS/E 4) will apply data mining techniques, perform statistical analysis, develop visualization techniques, and build automated tools that can assist with diagnosis, prognosis, and understanding of the pathophysiology of various ophthalmic diseases using machine learning. The RS/E 4 is expected to continue to develop their technical expertise and knowledge as it relates to data science, programming, machine learning, including reviewing recent literature, and implementing new techniques with limited supervision. The RS/E 4 is expected to take the lead on manuscript writing and assist with the preparation of grant proposals, and to develop independent research, if desired.

Requirements

Master's degree in Computer Science, Mathematics, Physics, Biostatistics or related field and four years of relevant experience.
Excellent scripting and programming skills including: Java, SQL, Python, Torch, Lua, Linux
Experience working in headless server environments
Desire to learn novel computational techniques and understanding of scaling processes
Proficiency with Microsoft Office suite of programs including Word, PowerPoint and Excel
Flexibility to learn new technologies, APIs, and SDKs by reading documentation
Excellent understanding of machine learning techniques and algorithms, such as k-NN, Naive Bayes, SVM, Decision Forests, etc.
Experience with common data science toolkits, such as R, Weka, NumPy, MATLAB, etc. Excellence in at least one of these is highly desirable
Excellent applied statistics skills, such as distributions, statistical testing, regression, etc.
Excellent oral and written communication skills, including presenting data and results at meetings
Comfortable leading multiple projects from start to finish
Ability to multitask, plan, and participate in different projects
Ability to solve well defined problems using accepted methods and techniques
Comply with NIH, Federal, state and University regulations on the safe and ethical use of animals and humans in research
Must have determination and a logical and independent mind to complete each project.
Must have very strong organizational abilities and experience in a team-focused work environment with excellent written and oral communication and interpersonal skills.

Nice To Haves

PhD degree in Computer Science, Mathematics, Physics, Biostatistics or related field AND at least four years of work experience in a related field OR five years of experience working in Biostatistics, Epidemiology or Computer Science. (Equivalent education/experience can substitute for minimum qualifications except where prohibited by legal requirements such as license/certificate/registration.)
Experience with Big Data analyses
Experience with common data science toolkits, such as R, Weka, NumPy, MATLAB, etc.
Familiarity with deep learning methods, such as CNNs and transformers
Experience with Linux system administration and knowledge of HIPAA compliance and FedRAMP 3PAO and NIST standards for security

Responsibilities

Conduct data mining using state-of-the-art methods.
Extend electronic medical record or imaging data with third-party sources of information when needed.
Enhance data collection and extraction procedures to include information relevant for building analytic systems.
Process, cleanse, and verify the integrity of data used for analysis.
Perform data munging tasks to reshape data for statistical analyses, visualization, and machine learning.
Perform data backup and basic computer hardware maintenance.
Select features, build, and optimize classifiers using machine learning techniques.
Perform statistical analyses with both traditional methods (e.g., survival analyses, mixed linear models, advanced imputation techniques) and machine learning approaches.
Conduct ad-hoc analyses and present results in a clear and concise manner.
Apply independent judgment to adapt and modify standard techniques, procedures, and criteria; devise new approaches to complex research problems as needed.
Review literature and recent advances in machine learning, data science, computer vision, and related research in ophthalmology.
Collaborate with investigators and team members to accomplish organizational goals and advance scientific knowledge.
Write scientific manuscripts for publication in peer-reviewed journals in collaboration with investigators at UW and other institutions, domestic and international.
Write or contribute to research manuscripts, reports, reviews, and summaries.
Perform other duties as assigned.