Research Assistant

About Company:

Elevate Clinical Research is a mission‑driven organization expanding access to high‑quality clinical trials across Texas, Louisiana, Illinois, Maryland, South Dakota, Hawaii, Kansas, and growing. We exist to bring research closer to the communities that need it most, delivering compassionate patient care, operational excellence, and trustworthy data that advances medicine.

At Elevate, your work has real impact. Every role contributes directly to improving health outcomes and bringing new therapies to life. We hire people who care deeply about patients, believe in doing things the right way, and want to grow with a company that’s expanding nationwide.

What We Value

• Patients first in every decision and interaction

• Access, equity, and bringing research to underserved communities

• High‑quality, compliant, reliable study execution

• Purpose‑driven growth and opportunities for advancement

• Supportive teams who communicate, collaborate, and care

Why People Join Us

• A mission that matters

• Clear, structured onboarding

• Leadership that listens and supports

• Career growth as we expand into new states

• A culture built on integrity, teamwork, and accountability

Join Elevate and help build a patient‑centered research network that’s changing what clinical trials can look like for communities across the country.

About the Role:

The Research Assistant will play a critical role in advancing healthcare and social assistance research by supporting data-driven studies and experimental protocols. This position involves collecting, processing, and analyzing complex datasets to uncover insights that can inform clinical practices and health policies. The successful candidate will collaborate closely with interdisciplinary teams to design and implement research methodologies, including both computational and laboratory techniques. They will contribute to the development and validation of machine learning models, particularly deep learning and neural networks, to interpret biomedical data. Ultimately, this role aims to facilitate impactful research outcomes that improve patient care and public health understanding.

Minimum Qualifications:

• Proficiency in data collection methods and laboratory techniques including gel electrophoresis.
• Experience with programming and data analysis tools such as TensorFlow, NumPy, and SPSS.
• Basic understanding of algorithms, neural networks, and deep learning concepts.
• Strong organizational skills and attention to detail in managing research data.

Preferred Qualifications:

• Master’s degree in a relevant discipline with a focus on health data analytics or computational biology.
• Prior experience in healthcare or social assistance research environments.
• Familiarity with advanced statistical methods and software for biomedical research.
• Experience publishing or contributing to peer-reviewed scientific research.
• Ability to work independently and as part of a multidisciplinary research team.

Responsibilities:

• Conduct comprehensive data collection through field research and laboratory experiments, ensuring accuracy and reliability.
• Utilize software tools such as TensorFlow, NumPy, and SPSS to preprocess, analyze, and visualize research data.
• Develop and implement algorithms and deep learning models to analyze complex biological datasets.
• Perform gel electrophoresis and other laboratory techniques to support experimental research objectives.
• Collaborate with research team members to design experiments, interpret results, and prepare reports and publications.

Skills:

The required skills such as data collection and field research are essential for gathering high-quality, relevant data that forms the foundation of all research activities. Proficiency in TensorFlow and NumPy enables the candidate to build and optimize machine learning models, particularly deep learning and neural networks, to analyze complex datasets effectively. SPSS skills are used to perform rigorous statistical analyses that validate research findings and support evidence-based conclusions. Laboratory skills like gel electrophoresis are applied to experimentally verify biological hypotheses and complement computational results. Together, these skills ensure the Research Assistant can integrate computational and experimental approaches to contribute meaningfully to healthcare research projects.