Associate Data Scientist

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.

The primary purpose of the Associate Data Scientist position is to provide support for 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 technical expertise.

This individual will 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 image analysis.

Successful candidates will develop computational methods using AI and deep learning, conduct data analysis and interpretation, collaborate with other data scientists, IT personnel and faculty together to address key clinical challenges that impact our patients.

Technical Expertise

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

Working with minimal oversight with researchers in analyzing, defining, and resolving analytical problems and bugs.

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

With direction, develops and maintains algorithms/tools and infrastructure for resolving specific analytical problems.

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

Keeping abreast of continually evolving analytical tools and strategies.

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.

Providing support for existing software systems as they evolve.

Analytical Thinking

Computational programming skills:

Preparing and running QA testing on new features/components.

Perform curation and analysis of data.

Test and containerize publicly available, pre-developed containers and models to enrich the TMI container library.

Working with end users to gather initial requirements.

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

Other duties as assigned

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

Preferred Education: Master's Level Degree

Certification: None

Preferred Certification: None

Experience Required: Three years of experience in scientific software development/analysis. With Master's degree, one year of experience required. With PhD, no experience required.

Preferred Experience: Experience with common open-source scientific computing/machine learning libraries (e.g., PyTorch / TensorFlow), containerization, and cloud-native technologies (Docker & Kubernetes) 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: 167295

• Employment Status: Full-Time

• Employee Status: Regular

• Work Week: Days

• Minimum Salary: US Dollar (USD) 85,000

• Midpoint Salary: US Dollar (USD) 106,500

• Maximum Salary : US Dollar (USD) 128,000

• FLSA: exempt and not eligible for overtime pay

• Fund Type: Soft

• Work Location: Remote (within Texas only)

• Pivotal Position: Yes

• Referral Bonus Available?: Yes

• Relocation Assistance Available?: Yes

• Science Jobs: No

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