Data Scientist (Research Associate, Non-Tenure Track Faculty)

Requisition #: A-

Status: Full Time

Type: Faculty

School: School of Medicine

Department/Program: Quantitative Sciences

The Sidney Kimmel Comprehensive Cancer Center at Johns Hopkins University fosters team science efforts, providing an environment where statisticians can advance standard statistical pipelines and cutting-edge quantitative science methodologies for state-of-art clinical studies. Thus, the members of the Division of Quantitative Sciences have strong collaborations with Cancer Center investigators and quantitative science Departments in the broader Johns Hopkins University, including the Departments of Biostatistics and Epidemiology in the Bloomberg School of Public Health. This collaborative, transdisciplinary research and educational environment promoted by the Cancer Center and the broader Johns Hopkins University fosters a diverse and inclusive community, featuring strong mentorship and a career-path for faculty statisticians.

The Data Scientist collaborates independently with principal investigators in statistical design and analysis of clinical and laboratory studies. Supervises and coordinates the work of staff statisticians, ensuring the appropriate and best use of standard and non-standard statistical methodologies for the design of studies and statistical analysis. Functions largely independently or as co-investigator to solve problems posed by clinical and laboratory investigators (including meetings, experimental design, analysis, and interim and final reports), and works with the Director of Oncology Quantitative Sciences Division and the Biostatistics Shared Resource Director of the Cancer Center to introduce and integrate novel analysis methods into the work of the statistical team as needed.

Specific Duties/Responsibilities:

1. Serve as the Lead Biostatistician in projects with standard designs
2. Participate in team science projects serving as statistician in the Biostatistics Core of SPOREs, P01s and U54s
3. Delegate duties to Biostatisticians working on the projects, providing coordination, oversight, direction and guidance as necessary
4. Monitor and coordinate the team's effort to meet project timelines and to ensure quality standards are met
5. Consult independently with clinical and laboratory investigators to determine their needs with respect to the statistical design of studies and propose appropriate designs and analysis methods
6. Collaborate with investigators in the preparation of presentations and publications, making significant contributions to publications
7. Determine, research, and implement appropriate methodologic procedures for analysis of complex data
8. Determine study design, perform sample size and power calculations, generate randomization schema, and write statistical sections for research protocols and manuscripts
9. Conduct statistical analysis and interpret the results. Prepare interim and final reports of studies. Discuss with colleagues, investigators, and supervisor the need for any additional analyses and/or special methodologic requirements
10. Manage computational workflows/pipelines to complete statistical or applied quantitative projects in an efficient and timely manner
11. Participate on the Protocol Review Committee

Supervision of Others:

Lead and supervise 1 - 2 junior Biostatisticians.

Qualifications

Minimum Qualifications:

Education :

- Required: Doctoral or Master's degree in biostatistics, statistics, mathematics, or related field

Experience

- Recent graduates with a PhD degree are welcome to apply

- Experienced in cancer clinical trials and/or other cancer-related studies including study design and data analysis preferred

Special Knowledge, Skills, and Abilities:

1. Excellent leadership and project management skills
2. Basic coaching and mentoring skills
3. Solid knowledge and expertise in statistics and its applications in laboratory, and clinical and translational research, demonstrated by publications
4. Experience with implementation of novel methods and/or innovation strategies
5. Organization and management skills: Ability to work independently and as part of a team; demonstrated time management skills; ability to multitask and assign priorities; demonstrated ability to manage highly detailed projects; ability to provide continuous quality and service improvement
6. Expert knowledge of procedural-oriented statistical languages, such as SAS, and/or R. In addition, a knowledge of other programming languages, such as Python is helpful
7. Excellent interpersonal skills in consulting with researchers and understanding their problems
8. Excellent writing and verbal skills in the preparation and presentation of reports. Attention to detail is required