Description

Data Scientist

Venesco LLC is looking for a Data Scientist with experience in Topological Data Analysis. In this role you will provide direct support the Internal Medicine Branch of USAFSAM Aerospace Medicine Consultation Division specifically to support cardiology data analysis. 

While this is a fully remote role, you would be required to pick up your equipment at Wright Patterson AFB in Dayton, OH prior to starting in this position. FOPR #24-27

The 711th Human Performance Wing (711 HPW), headquartered at Wright-Patterson Air Force Base in Ohio, is the first human-centric warfare wing to consolidate human performance research, education and consultation under a single organization. Established under the Air Force Research Laboratory, the 711 HPW is comprised of the Airman Systems Directorate (RH) and the United States Air Force School of Aerospace Medicine (USAFSAM). The Wing delivers unparalleled capability to the Air Force through a combination of world class infrastructure and expertise of its diverse workforce of military, civilian and contractor personnel encompassing 75 occupational specialties including science and engineering, occupational health and safety, medical professions, technicians, educators, and business operations and support.

Responsibilities

• 2.2.1 Design data modeling processes to create algorithms and predictive models and perform custom analysis and Identify trends in data sets.
• 2.2.2 Manipulate large data sets and use them to identify trends and reach meaningful conclusions based on research protocols.
• 2.2.3 Clean, aggregate, process, and organize data from disparate sources and transfer it to data warehouses while verifying the integrity of the data for analysis.
• 2.2.4 Provide expertise concerning the validity of assumptions made and the criteria by which alternatives are recommended, as well as advise and recommend new techniques, innovations, and technical and scientific advancements which may serve as a means of overcoming aeromedical operational issues.
• 2.2.5 Draft and/or edit study protocols, proposals, scientific abstracts, manuscripts, slide presentations, and posters as required.
• 2.2.6 Collaborate with professional colleagues, academic partners, medical professionals and government officials to achieve research goals. Interact professionally with all levels of personnel within and outside of the organization.
• 2.2.7 Organize review of relevant scientific literature and attend meetings, as required, with government organizations, academia, and meetings with other experts in their field.
• 2.2.8 Signed Non-Disclosure Agreement (NDA) in place with their company based on the potential access to confidential, proprietary, and/or sensitive information. The NDA shall be provided to the Government Program Manager by start of performance.

Requirements

Mandatory Qualifications

• 1.1.1.1 Master’s Degree in Data Science or related fields, for example Computer Science or Mathematics, with at least 2 years of experience in their field. Experience can be substituted with a PhD in Computer Science, Computational Geometry, or Data Science.
• 1.1.1.2 Demonstrated experience of application of machine learning algorithm methods on a variety of data types and sources.
• 1.1.1.3 Demonstrated experience of application of Topological Data Analysis methods on a variety of data types and source.
• 1.1.1.4 Demonstrated proficiency in the Python programing language.
• 1.1.1.5 Demonstrated expert knowledge in operations research and rigorous methods of scientific inquiry and analysis to include statistical analysis, machine learning, computer science, programming, and data storytelling.

Highly Desired Qualifications

1. 1.1.2.1. Have experience presenting research at scientific meetings and published in peer reviewed journals in Computational Geometry, Topological Data Analysis or Computational Topology.
2. 1.1.2.2 Publications relating to the subject of Topological Data Analysis
3. 1.1.2.3 Knowledge and application of the Topological Hierarchical Decomposition algorithm
4. 1.1.2.4. Prior military experience, or previous experience working in a military research