ARPA-H Computational Biologist - Oncology, Rare Diseases & Drug Discovery
ECS Corporate Services

Fairfax, Virginia
$100,000.00 - $180,000.00 per year


ECS is seeking an ARPA-H Computational Biologist - Oncology, Rare Diseases & Drug Discovery to work in our Remote office. Please Note: This position is contingent upon [contract award].

Job Description:

Are you a passionate, innovative Computational Biologist ready to tackle one of the biggest challenges in cancer treatments?

ECS is seeking talented and driven professionals to support the ADAPT (ADvanced Analysis for Precision cancer Therapy) program, a groundbreaking initiative by the Advanced Research Projects Agency for Health (ARPA-H) and led by the Resilient Systems Office (RSO).

About the ADAPT Program. ADAPT aims to bring together researchers in systems biology, bioinformatics, mathematical modeling, and oncology to address the challenge of treating ever-changing metastatic cancer. It will do this by developing solutions across three technical areas summarized as therapy recommendation techniques, new evolutionary clinical trials, and creation of a cancer treatment and analysis platform.

What you will do. In this role, you will serve as a "systems engineering and technical assistance" advisor to the RSO Program Manager (PM) by providing support and thought leadership to the PM, the performer teams, and stakeholders across the health innovation ecosystem in the research and development of revolutionary approaches that will adapt cancer treatments as tumors mutate and change. More broadly, you will enable the agency to manage and provide oversight to advanced R&D efforts; help shape future R&D concepts, programs, and solutions; integrate and advance our nation's healthcare ecosystem processes, policies, and enabling technologies; and enable the agency to expand its healthcare and scientific scope into yet unknown areas. This includes:

Serve as a scientific and technical advisor for cutting-edge research projects that will harness advanced technologies and a deep understanding of tumor biology to build cancer biomarkers.

Facilitate the identification, development and testing of solutions in three technical areas: therapy recommendation techniques, including multi-modal data fusion, resistant trait modeling, and development of biomarkers to predict drug response; new evolutionary clinical trials that treats patients during multiple therapy lines by discovering emergent resistant traits and therapy biomarkers developed by ADAPT; and the creation of a cancer treatment and analysis platform to enable real-time data availability.

Contribute your thought leadership to the design and delivery of architecture solutions and software applications that will support the development of these computational methodologies designed to analyze and predict utilizing machine learning algorithms and AI approaches.

Provide technical support via your expertise in computational biology and collaborate closely with the R&D team, their staff, and their software engineers to solve computing problems.

Support program managers in the development, technology transition, and commercialization of these new research methods, concepts, technologies, platforms, and processes.

Provide strategic assessments of methods and technologies related to the development of computational technologies and solutions.

Collaborate and support a team of performers across a range of expertise, such as researchers in systems biology, bioinformatics, mathematical modeling, and oncology to address the challenge of treating ever-changing metastatic cancer.

Join us and be a part of history!

Salary Range: $100,000-$180,000

General Description of Benefits

Required Skills:

  • Possess at least 6 years of total work experience (can be a combination of research, commercial, government, and consulting) for a Basic Scientist role; at least 7 years for an Intermediate Scientist role.
  • Proven experience in cancer or other rare disease research; or proven experience supporting research and/or development in areas of systems biology, bioinformatics, mathematical modeling, and oncology.
  • Experience in AI technology or molecular biosciences.
  • Experience designing, developing and/or modifying existing software to help solve routine, novel, and difficult scientific computing problems.
  • Demonstrated ability to conduct independent research, analyze data, write scientific reports, and evaluate new start proposals.
  • Passionate about innovation and driven to make a real impact on human health.
  • U.S. citizenship or lawful permanent residency is required per contract.
  • Ph.D. in Genetics, Chemical Biology, Chemistry, or related scientific field.

Desired Skills:
  • Experience in data analytics, oncology, or nucleic acid therapies is highly preferred.
  • One year or more experience working for a Federal ARPA organization.
  • One year or more experience in Federal Healthcare and/or Scientific Consulting.
  • Experience supporting complex healthcare and programs of a research and development (R&D) nature.
  • Strong communication and teamwork skills, with the ability to collaborate effectively with scientists, engineers, and other professionals from diverse backgrounds.
ECS is an equal opportunity employer and does not discriminate or allow discrimination on the basis of race, color, religion, sex, age, sexual orientation, gender identity or expression, national origin, ancestry, citizenship, genetic information, registered domestic partner status, marital status, status as a crime victim, disability, protected veteran status, or any other characteristic protected by law. ECS promotes affirmative action for minorities, women, disabled persons, and veterans.

ECS is a leading mid-sized provider of technology services to the United States Federal Government. We are focused on people, values and purpose. Every day, our 3800+ employees focus on providing their technical talent to support the Federal Agencies and Departments of the US Government to serve, protect and defend the American People.

General Description of Benefits



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