(While navigating through the site, please be sure to disable your pop-up blocker.)
Institute Associate Scientist IV Computational Biology
MD Anderson Therapeutics Discovery Division
Within The University of Texas MD Anderson Cancer Center lies the Therapeutics Discovery Division (TDD), a powerful engine driving the future of new targeted, immune- and cell-based therapies. Therapeutics Discovery eliminates the bottlenecks that hamper traditional drug discovery by employing a multidisciplinary team of dedicated researchers, doctors, drug developers, and scientific experts working together to develop small-molecule drugs, biologics, and cellular therapies. Our unique structure and collaborative approach allow the team to work with agility, bringing novel medicines from concept to clinic quickly and efficiently – all under the same roof.
The TRACTION platform
The Translational Research to AdvanCe Therapeutics and Innovation in ONcology (TRACTION) platform is an industrialized translational research group that aligns world-class drug discovery and development with highly innovative the science and clinical care research, for which MD Anderson Cancer Center is known. Through an investment in patient-centric research, we have developed the infrastructure, platforms, and capabilities to enable transformative research. TRACTION’s approach combines innovative cancer genetics, disruptive technologies, deep mechanistic biology, disease modeling, and pharmacology to accelerate the translation of novel discoveries into definitive clinical hypotheses. By partnering with the drug discovery engines within Therapeutics Discovery, we aim to advance a portfolio of small molecules, biologics, and cell therapies for our patients. We work in a fast-paced, milestone-driven environment with a focus on team science and interdisciplinary research. Our unique approach has created a biotech-like engine within the walls of the nation’s leading cancer center to bring life-saving medicines to our patients more quickly and effectively.
Position Description
We are seeking a highly skilled and detail-oriented data operations analyst to join our team as an Associate Scientist of Computational Biology. This position will support our computational team in the development and application of bioinformatic workflows for processing, management, and integration of our large volume of genomic and imaging data and improving their Findability, Accessibility, Interoperability, and Reuse (FAIR). An ideal candidate will work closely with biologists and computational scientists to support our programs with data analysis and integration to uncover insights that can be translated into safe and effective therapies. In addition, the candidate will learn to design and execute computational experiments to evaluate statistical methods and integration strategies that can accelerate the impact of computational analysis.
For this role, an important measure success is the ability to drive the application of computational biology tools to solve problems in a team-science environment that enable hypothesis-driven testing of oncology therapeutics and/or biomarker strategies in the clinical setting. Overall, the improvement of the team’s data assets will have a rapid and direct impact on patient care.
Key Function
* Independently design, build, and test systems to reproducibly process, store, and integrate large-scale datasets.
* Deploy and manage data analysis pipeline for omics or imaging data.
* Work with teams to collect and validate meta data.
* Independently evaluate, update, and modify existing data applications.
* Maintain and test cleanly written code in github and collaborate with team members to further optimize.
* Document development of analytical approaches with computational notebook applications (i.e Jupyter Notebooks).
* Develop strong collaborative relationships with internal and external groups.
* Interpret, present, and report research findings at internal meetings and external scientific conferences.
* Use analytical thinking skills to break down problems into workable solutions.
* Leverage design thinking techniques to engage users and coordinately develop innovative protype-systems to solve problems and evolve these concepts into production applications.
* Work well under pressure and drive projects that impact critical timelines.
EDUCATION:
Required: Bachelor's degree in Biology, Biochemistry, molecular biology, cell biology, enzymology, pharmacology, chemistry or related field.
Preferred: Master's degree in Computer Science, Engineering, Applied Mathematics, Biostatistics or a related discipline from an accredited university.
EXPERIENCE:
Required: Five years of relevant research experience in lab. With preferred degree, three years of required experience.
Preferred Candidate will possess the following:
* Master’s degree with >3 years of relevant post-degree experience in a pharmaceutical/biotech environment.
* Extensive experience with code management (GitLab or GitHub), and computational notebook solutions.
* Evidence of successfully contributing or leading software development projects in a professional environment.
* Familiar with FAIR guiding principles and their application to data science projects.
* Evidence of proficiency in programing languages (Python, R, JavaScript), scripting languages (Bash), high-performance computing, and database management systems.
* Experience with cloud computing, container images, reproducible workflows, interactive visualization, and machine learning.
* Outstanding organizational skills and the ability to effectively present results and conclusions to co-workers, collaborators, and manager.
* Experience with tools and best practices in applied genomics and / or digital pathology
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
