The Department of Ophthalmology at Stanford University School of Medicine is seeking a highly motivated, hard-working and professional Data Scientist to facilitate research efforts in ophthalmology. The incumbent will be part of the department of ophthalmology: however, the position will be in a collaborative environment, engaging with other Stanford faculty and staff across multiple departments, including Biomedical Informatics, Research IT, and Research Informatics Center. The incumbent will work. with a combination of structured and unstructured (text, imaging) data from several sources, including Stanford’s STARR and STARR-OMOP clinical research databases, the ophthalmology IRIS (Intelligent Research In Sight) national clinical data registry, commercial and Medicare claims data, national survey data, and other sources.

The position will require an incumbent who is comfortable working with some independence; consulting with and advising investigators to refine research questions, define hypotheses and project objectives, design studies and devise analysis plans; and

working with project team members—including clinicians, trainees, and other statisticians/informaticists—to implement analysis plans and publish findings. The incumbent must be proficient at balancing involvement in multiple simultaneous projects and prioritizing to manage competing priorities. The incumbent will work closely with others to interrogate databases to create analytic files, perform quality control and data cleaning, and manage and analyze data. The incumbent must be an excellent and timely communicator, able to present results in oral and written form to clinical investigators.

Duties include:

Collect, manage and clean datasets.Employ new and existing tools to interpret, analyze, and visualize multivariate relationships in data.Create databases and reports, develop algorithms and statistical models, and perform statistical analyses appropriate to data and reporting requirements.Use system reports and analyses to identify potentially problematic data, make corrections, and determine root cause for data problems from input errors or inadequate field edits, and suggest possible solutions.Develop reports, charts, graphs and tables for use by investigators and for publication and presentation.Analyze data processes in documentation.Collaborate with faculty and research staff on data collection and analysis methods.Provide documentation based on audit and reporting criteria to investigators and research staff.Communicate with government officials, grant agencies and industry representatives. - Other duties may also be assigned

The job duties listed are typical examples of work performed by positions in this job classification and are not designed to contain or be interpreted as a comprehensive inventory for all duties, tasks, and responsibilities. Specific duties and responsibilities may vary depending on department or program needs without changing the general nature and scope of the job or level of responsibility. Employees may also perform other duties as assigned.

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.

The expected pay range for this position is$82,468 to $106,256 annually. Stanford University provides pay ranges representing its good faith estimate of what the university reasonably expects to pay for a position. 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.

DESIRED QUALIFICATIONS:

Strong background in machine learning, biostatistics, and bioinformaticsIntellectually curious; willing and eager to learn new skillsExperience with large datasets and database useExperience with analysis of real-world observational health data (e.g., electronic medical records, insurance claims)Manipulation and analyses of complex high-dimensional dataAbility to perform careful data cleaning and preparation, including: identifying and handling data discrepancies, duplicates, missing values, outliers, etc; developing cohorts of patients based on inclusion and exclusion criteria, such as those based on billing code diagnoses, age or other demographics, length of follow-up, or other characteristics; creating new variables, including coding relevant outcomes, combining sparse variables, normalizing/standardizing variables; merging datasets on multiple key values; reshaping data from long to wide or vice versa as the befits the analysis needs; loading data into analysis programs, saving data into different file formatsExperience with at least 2 of the following: 1) Machine learning predictive models (gradient boosted trees, random forest etc.); 2) Deep learning neural networks, transfer learning; 3) Hierarchical/multilevel modeling, propensity score matching/weighting 4) Convolutional neural networksExperience with free-text data (e.g., natural language processing) is a plus, or else willingness to learnUse of R or SAS (preferred) or STATA

EDUCATION & EXPERIENCE (REQUIRED):

Bachelor's degree or a combination of education and relevant experience. Experience in a quantitative discipline such as economics, finance, statistics or engineering.

KNOWLEDGE, SKILLS AND ABILITIES (REQUIRED):

Substantial experience with MS Office and analytical programs.Strong writing and analytical skills.Ability to prioritize workload.

CERTIFICATIONS & LICENSES:

None

PHYSICAL REQUIREMENTS*:

Sitting in place at computer for long periods of time with extensive keyboarding/dexterity.Occasionally use a telephone.Rarely writing by hand.

* - Consistent with its obligations under the law, the University will provide reasonable accommodation to any employee with a disability who requires accommodation to perform the essential functions of his or her job.

WORKING CONDITIONS:

Some work may be performed in a laboratory or field setting.

Additional WORKING CONDITIONS: May work extended or non-standard hours based on project or business cycle needs.