Data Scientist – Operations Research

MISSION STATEMENT



The University of Texas MD Anderson Cancer and its Institute for Data Science in Oncology (IDSO) includes five major focus areas for the development and application of data science and emerging medical technologies for positive impact in clinical care and operations. Within the IDSO focus area on Safety, Quality, and Access is a program for operations research in healthcare. The team includes Data Scientists working in areas of systems engineering, technology development, and development and implementation of machine learning / deep learning methods for translation to clinical application.



SUMMARY



The Data Scientist – Operations Research position plays a critical role in driving operational innovation through the development of advanced automated solutions that enhance patient access, quality of care, and safety.  In collaboration with physicians, nurses, and administrative staff, the Data Scientist will be responsible for developing and evaluating mathematical / statistical / computational techniques to analyze complex problems and promote data-driven decision making to improve the safety, quality, efficiency, and value across clinical operations. Primary areas of impact include, but are not limited to, surgery, chemotherapy, outpatient, and inpatient care services.

 

JOB SPECIFIC COMPETENCIES



The chosen applicant should have demonstrated experience with computing/programming (Python, Matlab, R, C++, Java, and/or SQL), machine learning / deep learning methods including neural network design, and statistical analysis. Preference is for candidates with background in data science, with particular interest in candidates with knowledge and experience in optimization, working with large datasets, and operations research. Preferred experience with common open source scientific computing/machine learning libraries including: PyTorch / TensorFlow, pandas, scipy, scikit, PuLP, Pyomo, and Gurobi. Experience with database languages such as SQL is also desirable. The candidate should also have experience with multiple modern techniques and algorithms in machine learning and statistical computing such as regression, clustering/segmentation, time-series analysis, recommender systems, predictive analytics, Bayesian modeling, data fusion, supervised/unsupervised learning, decision trees, A/B testing, and natural language processing.

 

Responsibilities:  

- Develop algorithms and mathematical models to support decision making in clinical operations.
- Collaborate and consult with clinical staff in defining, analyzing, and resolving analytical problems.
- Design, develop and implement algorithms and software.
- Develop key model performance metrics.
- Design and implement experiments to test hypotheses and validate models.
- Analyze and interpret large datasets to identify trends and patterns.
- Develop and implement machine learning algorithms to automate processes and improve decision-making.
- Coordinate the creation of robust, user-friendly software tools that can be deployed in a clinical setting.
- Work with end users to gather initial requirements, including clinicians, nurses, administrators, and other stakeholders.
- Work with other data scientists in multidisciplinary research related to systems engineering, technology development, and data-intensive methods applied to surgery, interventional radiology, and diagnostic imaging.
- Provide support for existing software systems as they evolve.
- Keep up to date with the latest developments in operations research and optimization techniques.
- Communicate with other team members as well as research and clinical stakeholders throughout the institution to share information and best practices.

 

Data analysis and reporting  


- Familiarity with modern object-oriented programming languages such as Python, C++, and/or Java 
- Experience with open source optimization packages as well as other optimization software packages such as Gurobi, CPLEX, or MOSEK 
- Experience with large scale mixed-integer linear programming
- Data extraction, transformation, and loading activities where needed to translate to and from relational data sets
- Communicate results in clear written and/or oral formats at internal and external meetings and conferences
- Must be able to fully communicate results and explain technical concepts to non-technical stakeholders.
- Ability to work independently and manage multiple projects simultaneously

 

Collaboration and Presentation   


- Interact and participate in collaborative studies inside and outside MD Anderson.
- Present research results at internal and external meetings and conferences
- Prepare final reports in the format requested for the data generated by suggested due date  
- Learn and help others in the implementation of new techniques