The mission of The University of Texas M. D. Anderson Cancer Center is to eliminate cancer in Texas, the nation, and the world through outstanding programs that integrate patient care, research, and prevention, and through education for undergraduate and graduate students, trainees, professionals, employees, and the public.

The University of Texas MD Anderson Cancer Center has the potential to unlock the power of data by further developing and investing in talent, team science and infrastructure to optimize multidimensional data integration, analysis, and application for the benefit of patients with cancer.

Dr. Caroline Chung leads an imaging computational laboratory within the Department of Radiation Oncology at MD Anderson Cancer Center. The Chung lab's major research focus is to develop quantitative imaging pipelines and predictive tools to be used in: 1) tumor response assessment; 2) treatment-related toxicity; and 3) personalization of radiotherapy and multimodal treatment. In addition, the lab is working on the standardization of collection and nomenclature of images to facilitate meaningful measurement and interpretation of imaging biomarkers across departments and institutions to support efforts aligned with the Institute for Data Science in Oncology.

Quantitative imaging research is a key component to enabling and guiding personalized oncological patient care. The Chung Lab has an additional role in supporting the Tumor Measurement Initiative (TMI), which aims to build an institutional platform to support standardized, automated, quantitative imaging-based tumor measurement across each patient's journey to advance multidisciplinary, data-driven, high precision cancer treatment.

The primary purpose of the Assistant Data Scientist position is to contribute to scientific projects, assist researchers with their independent RD projects, provide support for building pipelines and tools to support data curation, analysis, extraction of tumor measurements and building predictive tools for tumor response and toxicity to normal tissues. This activity requires a combination of computational modeling skills, technical expertise in quantitative medical imaging, understanding of oncology and oncology treatment and strong teaming and communication.

This individual will have demonstrated experience with programming languages and scripting methods (Python, R, MATLAB), machine learning / deep learning methods, data analytics, and advanced image analysis.

Successful candidates will collaborate with other data scientists, IT personnel, collaborating faculty and trainees to address key clinical challenges that impact our patients. The successful candidate will also engage in teaching activities within the team, as well as assist in the submission of grant proposals and research publications.

JOB SPECIFIC COMPETENCIES

Drive: Technical Expertise

Working with researchers to develop, adapt, and implement computational methods by applying deep learning methods and architectures for the datasets.

Participating in discussion and implementation of machine learning model management solutions.

Performing, with supervision, data- and image processing and analysis.

Working with faculty, IT personnel, and other researchers to respond to new technologies.

Keeping abreast of continually evolving analytical tools and strategies.

Assisting with problem solving and data science work for research projects by assessing the challenge/need, generating automation processes and tools to gain efficiency, developing modular solutions that contribute to larger projects etc.

Maintaining high code quality and ensuring code is thoroughly and consistently tested before deploying for end-user use.

Organizing data and publishing code with documentation, in line with departmental standards.

Assisting with documentation of data science related processes, including the use of the relevant computational tools.

Drive: Analytical Thinking

Computational programming skills.

Preparing and running QA/QC testing on new features/components.

Performing data curation, data annotation, and data management tasks.

Assisting researchers in analyzing, defining, and resolving analytical problems and bugs.

Assisting with figure generation, as well as abstract and manuscript preparation.

Working with end users to gather initial requirements.

Assisting researchers in analyzing a wide variety of clinical data, design, feasibility testing of proposed solutions, evaluate and interpret the results.

Professionalism: Oral and Written Communication

Presenting results in collaboration meetings and communicating with other team members to share information and tips.

Communicating and assisting cooperatively and effectively with leaders, peers, end users and support teams when required.

Participating in teaching activities within the team, e.g. by providing tutorials to other lab members when needed.

Other duties as assigned.

Education

Required: Bachelor's degree in Biomedical Engineering, Electrical Engineering, Computer Engineering, Physics, Applied Mathematics, Statistics, Computer Science, Computational Biology, or related field.

Preferred Experience: Experience with common open-source scientific computing/machine learning libraries (e.g., PyTorch / TensorFlow), radiation treatment-planning software (e.g. RayStation), medical imaging (e.g. DICOM), and radiomics analysis is preferred.

It is the policy of The University of Texas MD Anderson Cancer Center to provide equal employment opportunity without regard to race, color, religion, age, national origin, sex, gender, sexual orientation, gender identity/expression, disability, protected veteran status, genetic information, or any other basis protected by institutional policy or by federal, state or local laws unless such distinction is required by law. http://www.mdanderson.org/about-us/legal-and-policy/legal-statements/eeo-affirmative-action.html

Additional Information

Requisition ID: 173148

Employment Status: Full-Time

Employee Status: Regular

Work Week: Days

Minimum Salary: US Dollar (USD) 66,000

Midpoint Salary: US Dollar (USD) 82,500

Maximum Salary : US Dollar (USD) 99,000

FLSA: exempt and not eligible for overtime pay

Fund Type: Soft

Work Location: Onsite

Pivotal Position: Yes

Referral Bonus Available?: Yes

Relocation Assistance Available?: Yes

Science Jobs: Yes

#LI-Onsite