ML Data Scientist (Medical Imaging) - MD Anderson
Houston, TX
About the Job
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.
The primary purpose of the Data Scientist position is to design, implement and maintain the process of building automated image interpretation tools and the extraction of tumor measurements to fulfill the TMI objective. This activity is an important sub-component of the overall function of TMI and requires a combination of computational skill and subject matter (imaging) expertise.
Ideal candidates will have experience developing and training 3D models using radiology images.
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) 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.
This individual will be working with internal and external teams developing specific image analysis algorithms and will coordinate these efforts in a fashion that supports scientific/technical evaluation and integration into the broader TMI effort.
They will also have demonstrated experience with programming languages and scripting methods (Python, MATLAB, C++, CUDA, Bash, and/or SQL), machine learning / deep learning methods, data analytics, and medical image analysis. Preference is for candidates with experience with common open-source scientific computing libraries such as PyTorch and TensorFlow. The ideal candidate will have strong computational and analytical skills particularly in deep learning and is motivated by solving challenging medical research problems for patient benefit. Additionally, experience identifying opportunities to streamline and optimize code and imaging pipeline processes is a plus. Interest in continuously and independently exploring and learning new technologies and solutions beyond current knowledge base is also required.
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. The Institute for Data Science in
Oncology (IDSO) is a signature priority program aimed at transforming the patient experience, enhancing quality of life and accelerating scientific breakthroughs via advanced, data-driven approaches to cancer care. IDSO will enable teams to search for, learn from and apply as much information as possible gathered from
every patient MD Anderson has seen or will see. By growing a data-centric culture and advancing data management and analytics maturity, we provide better, state-of-science and state-of-data-science care for patients while exploring areas of cancer research and treatment currently unknown to clinical communities.
The IDSO recruits, positions and enables best-in-class data scientists to unravel seemingly insoluble problems in cancer and work toward meaningful solutions for patients. Our aims include reducing the time between diagnostic procedures and treatment decisions, advancing drug discovery efforts and bringing new, precise medicines to bedsides sooner. The IDSO centralizes our focused institutional investment in data science, as well as enables partnerships with other world-leading organizations, to operate and enhance an unprecedented oncological "data supply chain" designed to accelerate research and treatment innovation. Comprised of the best minds in a myriad of scientific and data-driven fields, the IDSO facilitates a culture grounded in our innovative "team-data science" principles, such as shared motivation, shared learning, provenance linking insights to observations and integrated data governance.
Quantitative imaging research is a key component to enabling and guiding personalized oncological patient care. In support of the objectives of the IDSO, the Tumor Measurement Initiative (TMI) 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.
Other duties as assigned
Education: Required Bachelor's degree with a concentration in Science, Engineering or related field.
Preferred Education: Master's Level Degree
Experience Required: Three years experience in scientific software or industry development/analysis. Master's degree, one year of experience required. With PhD, no experience required.
Preferred Experience:
Experience developing and training 3D models using radiology images.
Experience with common open-source scientific computing/machine learning libraries (e.g., PyTorch / TensorFlow), containerization, and cloud-native technologies (Docker & Kubernetes) is preferred.
Knowledge of version control protocols, automated test frameworks, and high-performance computing is highly desired.
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
#LI-Hybrid
The primary purpose of the Data Scientist position is to design, implement and maintain the process of building automated image interpretation tools and the extraction of tumor measurements to fulfill the TMI objective. This activity is an important sub-component of the overall function of TMI and requires a combination of computational skill and subject matter (imaging) expertise.
Ideal candidates will have experience developing and training 3D models using radiology images.
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) 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.
This individual will be working with internal and external teams developing specific image analysis algorithms and will coordinate these efforts in a fashion that supports scientific/technical evaluation and integration into the broader TMI effort.
They will also have demonstrated experience with programming languages and scripting methods (Python, MATLAB, C++, CUDA, Bash, and/or SQL), machine learning / deep learning methods, data analytics, and medical image analysis. Preference is for candidates with experience with common open-source scientific computing libraries such as PyTorch and TensorFlow. The ideal candidate will have strong computational and analytical skills particularly in deep learning and is motivated by solving challenging medical research problems for patient benefit. Additionally, experience identifying opportunities to streamline and optimize code and imaging pipeline processes is a plus. Interest in continuously and independently exploring and learning new technologies and solutions beyond current knowledge base is also required.
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. The Institute for Data Science in
Oncology (IDSO) is a signature priority program aimed at transforming the patient experience, enhancing quality of life and accelerating scientific breakthroughs via advanced, data-driven approaches to cancer care. IDSO will enable teams to search for, learn from and apply as much information as possible gathered from
every patient MD Anderson has seen or will see. By growing a data-centric culture and advancing data management and analytics maturity, we provide better, state-of-science and state-of-data-science care for patients while exploring areas of cancer research and treatment currently unknown to clinical communities.
The IDSO recruits, positions and enables best-in-class data scientists to unravel seemingly insoluble problems in cancer and work toward meaningful solutions for patients. Our aims include reducing the time between diagnostic procedures and treatment decisions, advancing drug discovery efforts and bringing new, precise medicines to bedsides sooner. The IDSO centralizes our focused institutional investment in data science, as well as enables partnerships with other world-leading organizations, to operate and enhance an unprecedented oncological "data supply chain" designed to accelerate research and treatment innovation. Comprised of the best minds in a myriad of scientific and data-driven fields, the IDSO facilitates a culture grounded in our innovative "team-data science" principles, such as shared motivation, shared learning, provenance linking insights to observations and integrated data governance.
Quantitative imaging research is a key component to enabling and guiding personalized oncological patient care. In support of the objectives of the IDSO, the Tumor Measurement Initiative (TMI) 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.
Other duties as assigned
Education: Required Bachelor's degree with a concentration in Science, Engineering or related field.
Preferred Education: Master's Level Degree
Experience Required: Three years experience in scientific software or industry development/analysis. Master's degree, one year of experience required. With PhD, no experience required.
Preferred Experience:
Experience developing and training 3D models using radiology images.
Experience with common open-source scientific computing/machine learning libraries (e.g., PyTorch / TensorFlow), containerization, and cloud-native technologies (Docker & Kubernetes) is preferred.
Knowledge of version control protocols, automated test frameworks, and high-performance computing is highly desired.
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: 170771
- Employment Status: Full-Time
- Employee Status: Regular
- Work Week: Days
- Minimum Salary: US Dollar (USD) 103,000
- Midpoint Salary: US Dollar (USD) 129,000
- Maximum Salary : US Dollar (USD) 155,000
- FLSA: exempt and not eligible for overtime pay
- Fund Type: Soft
- Work Location: Hybrid Onsite/Remote
- Pivotal Position: Yes
- Referral Bonus Available?: Yes
- Relocation Assistance Available?: Yes
- Science Jobs: No
#LI-Hybrid
Source : MD Anderson